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591
Towards a Science of Security GamesKey Algorithmic Principles Deployed Systems Research Challenges
Milind TambeHelen N and Emmett H Jones Professor in Engineering
University of Southern Californiawith
Currentformer PhD studentspostdocs
Bo An Matthew Brown Francesco Delle Fave Fei
Fang Benjamin Ford William Haskell Manish Jain
Albert Jiang Debarun Kar Chris Kiekintveld Rajiv
Maheswaran Janusz Marecki Praveen Paruchuri
Jonathan PearceJames Pita Thanh Nguyen Yundi
Qian Eric Shieh Jason Tsai Pradeep Varakantham
Haifeng Xu Amulya Yadav Rong Yang Zhengyu
Yin Chao Zhang
Other collaborators Fernando Ordonez (USC amp U Chile) Richard John
(USC) David Kempe (USC) Shaddin Dughmi
(USC) amp
Craig Boutilier (Toronto) Jeff Brantingahm
(UCLA) Vince Conitzer (Duke) Sarit Kraus (BIU
Israel) Andrew Lemieux (NCSR)Kevin Leyton-
Brown (UBC) M Pechoucek (CTU Czech R) Ariel
Procaccia (CMU) Tuomas Sandholm (CMU)
Martin Short (GATech) Y Vorobeychik
(Vanderbilt) hellip
59
Global Challenges for Security
Game Theory for Security Resource Optimization
2
59
Example Model
Stackelberg Security Games
Security allocation
Targets have weights
Adversary surveillance
Target
1
Target
2
Target 1 4 -3 -1 1
Target 2 -5 5 2 -1
Adversary
3
Defender
59
Example Model
Stackelberg Security Games
Security allocation
Targets have weights
Adversary surveillance
Target
1
Target
2
Target 1 4 -3 -1 1
Target 2 -5 5 2 -1
Adversary
4
Defender
59
Example Model
Stackelberg Security Games
Security allocation
Targets have weights
Adversary surveillance
Target
1
Target
2
Target 1 4 -3 -1 1
Target 2 -5 5 2 -1
Adversary
5
Defender
59
Stackelberg Security GamesSecurity Resource Optimization Not 100 Security
Random strategy
Increase costuncertainty to attackers
Stackelberg game
Defender commits to mixed strategy
Adversary conducts surveillance responds
Stackelberg Equilibrium Optimal random
Target
1
Target
2
Target 1 4 -3 -1 1
Target 2 -5 5 2 -1
Adversary
6
Defender
59
Research Contributions
Game Theory for Security
Computational Game Theory in the Field
Computational game
theory
bull Massive games
Behavioral game
theory
bull Exploit human
behavior
models
+ Planning under uncertainty learninghellip
7
859
Ports amp port traffic
US Coast Guard
Applications Deployed Security Assistants
Airports access
roads amp flights
TSA
Airport Police
Urban transport
LA SheriffrsquosTSA
Singapore Police
Environment
US Coast Guard
WWF WCShellip
59
Key Lessons Security Games
Decision aids based on computational game theory in daily use
Optimize limited security resources against adversaries
Applications yield research challenges Science of security games
Scale-up Incremental strategy generation amp Marginals
Uncertainty Integrate MDPs Robustness Quantal response
Current applications (wildlife security) Interdisciplinary challenge
Global challenges Merge planninglearning amp security games
9
5910
Outline ldquoSecurity Gamesrdquo Research (2007-Now)
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
Evaluation II Real-world deployments
(Patience)
Evaluation I Scale up Handle uncertainty
Publications
AAMAS AAAI IJCAIhellip
2007 onwards
5911
bull 6 plots against LAX
ARMOR LAX (2007) GUARDS TSA (2011)
Airport Security Mapping to Stackelberg Games
GLASGOW 63007
5912
ARMOR Operation [2007]Generate Detailed Defender Schedule Pita Paruchuri
Mixed Integer Program
Pr(Canine patrol 8 AM Terminals 357) = 033
Pr(Canine patrol 8 AM Terminals 256) = 017
helliphellipCanine Team Schedule July 28Term 1 Term 2 Term 3 Term 4 Term 5 Term 6 Term 7 Term 8
8 AM Team1 Team3 Team5
9 AM Team1 Team2 Team4
10 AM Team3 Team5 Team2
Target 1 Target 2
Defender 1 2 -1 -3 4
Defender 2 -3 3 3 -2
5913
ARMOR MIP [2007]Generate Mixed Strategy for Defender
1 i
ixts
jq
ix
ijR
Xi Qj
max
1Qj
jq
MqxCa ji
Xi
ij )1()(0
Maximize defender
expected utility
Defender mixed
strategy
Adversary best
response
Pita Paruchuri
Target 1 Target 2
Defender 1 2 -1 -3 4
Defender 2 -3 3 3 -2
Adversary response
5914
ARMOR Payoffs [2007]Previous Research Provides Payoffs in Security Game Domains
Target 1 Target 2
Defender 1 2 -1 -3 4
Defender 2 -3 3 3 -2
jq
ix
ijR
Xi Qj
maxMaximize defender
expected utility
5915
ARMOR MIP [2007]Solving for a Single Adversary Type
Term 1 Term 2
Defend1 2 -1 -3 4
Defend2 -3 1 3 -3
1 i
ixts
jq
ix
ijR
Xi Qj
max
1Qj
jq
ljq][ix
MqxCa ji
Xi
ij
1010
)1()(0
Maximize defender
expected utility
Defender strategy
Adversary strategy
Adversary best
response
119909119894
ARMORhellipthrows a digital cloak of invisibilityhellip
59
IRIS Federal Air Marshals Service [2009]Scale Up Number of Defender Strategies
1000 Flights 20 air marshals 1041
combinations
ARMOR out of memory
Strategy 1 Strategy 2 Strategy 3
Strateg
y 1
Strateg
y 2
Strateg
y 3
Strateg
y 4
Strateg
y 5
Strateg
y 6
Not enumerate all combinations
Branch and price Incremental strategy generation
Strategy 1 Strategy 2 Strategy 3
Strategy 1
Strategy 2
Strategy 3
Strategy 4
Strategy 5
Strategy 6
16
59
IRIS Scale Up Number of Defender Strategies [2009]
Small Support Set for Mixed Strategies
Small support set size
bull Most xi variables zero
x124=0239
x123=00
x135=00
Attack
1
Attack
2
Attack
hellip
Attack
1000
123 5-10 4-8 hellip -209
124 5-10 4-8 hellip -209
135 5-10 -95 hellip -209
hellip
hellip1041 rowsx378=0123
10]10[
)1()(0
11
max
jqx
MqxCa
qxts
qxR
i
ji
Xi
ij
Qj
j
i
i
jiij
Xi Qj
qx
1000 flights 20 air marshals
1041combinations
17
59
Target 3 Target 7
hellip hellip
Resource Sink
Best new pure strategy
Minimum cost network flow
IRIS Incremental Strategy Generation
Exploit Small Support
Attack 1 Attack 2 Attack Attack 6
124 5-10 4-8 hellip -209
378 -8 10 -810 hellip -810
hellip
500 rows
NOT 1041
Attack 1 Attack 2 Attackhellip Attack 6
124 5-10 4-8 hellip -209
Slave (LP Duality Theory)
Master
Converge
GLOBAL
OPTIMAL
Attack 1 Attack 2 Attackhellip Attack 6
124 5-10 4-8 hellip -209
378 -8 10 -810 hellip -810
18
Jain Kiekintveld
59
IRIS Deployed FAMS (2009-)
ldquohellipin 2011 the Military Operations Research Society selected a University
of Southern California project with FAMS on randomizing flight
schedules for the prestigious Rist Awardhelliprdquo
-R S Bray (TSA)
Transportation Security Subcommittee
US House of Representatives 2012
19
Significant change in operations
59
Networks Mumbai Police Checkpoints[2013]
150 edges 2 Checkpoints
150-choose-2 strategies
With V Conitzer 20
Jain
59
Networks Mumbai Police Checkpoints[2013]
Incremental Strategy Generation
With V Conitzer 21
Jain
Double oracle Converge to a global optimal
Path 1 Path 2
Checkpoint
strategy 15 -5 -1 1
Path 1 Path 2
Checkpoint
strategy 15 -5 -1 1
Checkpoint
strategy 2-5 5 2 -1
Defender oracle
Path 1 Path 2 Path 3
Checkpoint
strategy 15 -5 -1 1 -2 2
Checkpoint
strategy 2-5 5 1 -1 -2 2
Attacker oracle
Path 1 Path 2 Path 3
Checkpoint
strategy 15 -5 -1 1 -2 2
Checkpoint
strategy 2-5 5 1 -1 -2 2
59
Double Oracle[2013]Incremental Strategy Generation Exploit Small Support
22
150 edges 2 Checkpoints
Only six candidate edges
for checkpoints
Jain
5923
Mumbai Police Checkpoints[2013]Results of Scale-up
20416 Roads15 checkpoints
20 min
59
Double Oracle Social Cyber Networks[2013]
Incremental Strategy Generation
24
Social networkseg counter-insurgency
Sources
Intermediate
Nodes
Links
Targets
Cyber networks
Tsai
59
Outline ldquoSecurity Gamesrdquo Research
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
25
59
Port Security Threat Scenarios
US Ports $315 trillion economy
USS Cole after suicide attack
French oil tanker hit by small boat
Attack on a ferry
26
59
PROTECT Randomized Patrol Scheduling [2013]
27
Port Protection (Scale-up) and Ferries (Continuous Spacetime)
59
PROTECT Randomized Patrol Scheduling [2013]Port Protection (Scale-up) and Ferries (Continuous Spacetime)
28
59
Ferries Scale-up with Mobile Resources amp Moving TargetsTransition Graph Representation
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
29
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Ferries Scale-up with Mobile Resources amp Moving TargetsTransition Graph Representation
30
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
31
Patrols protect nearby ferry location Solve as done in ARMOR
Ferries Patrol RoutesExponential Numbers of Patrol Routes
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Patrols protect nearby ferry location Solve as done in ARMOR
Ferries Patrol RoutesExponential Numbers of Patrol Routes
32
Pr([(B5) (C 10) (C15)]) = 017
Pr([(B5) (C10) (B15)]) =013
NT variables
Pr([(A5) (A10) (B15)]) = 007
Pr([(A5) (A10) (A15)]) = 003
Jiang KiekintveldFang
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Variables NOT routes but probability flow over each segment
Ferries Scale-up Marginal Probabilities Over Segments
33
NT variables
Jiang KiekintveldFang
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
030
017
013
Obtain marginal probabilities over segments
010
007
003
N2T variablesExtract Pr([(B5) (C 10) (C15)]) = 017
Pr([(B5) (C10) (B15)]) =013
Ferries Scale-up with Marginals Over Separable SegmentsSignificant Speedup
34
NT variables
59
5936
Outline ldquoSecurity Gamesrdquo Research (2007-Now)
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
Evaluation II Real-world deployments
(Patience)
Evaluation I Scale up Handle uncertainty
59
TRUSTS Frequent adversary interaction games
Patrols Against Fare Evaders
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
017
013
010
010
37
59
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
38
Jiang Delle Fave
59
Markov Decision Problems in Security games
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
39
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution Jiang Delle Fave
5940
Urban Transportation Security
MDPs amp DEC-MDPs in Security Games
COPS LA Metro System(Against Opportunistic Crime)
STREETS Singapore Roads(Against Reckless Driving)
DEC-MDPs in Security Games Teamwork + Uncertainty
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
ZhangShieh
59
Uncertainty Space Algorithms
Bayesian and Robust Approaches
Adversary payoff uncertainty
Adversary observation amp
defender execution
uncertainty
Adversary rationality
uncertainty
RECON
BRASS
Monotonic Maximin
(Monotonic adversary)
41
URAC
Payoff interval
Not point
estimateGMC HUNTER
Bayesian
Robust
Yin Nguyen An
0
05
1
15
5 10
Def
end
ers
EU
Targets
ISG RECON URAC
59
Security Games Environmental Crime
amp Bounded Rationality
FisheryGulf of Mexico
Nakai Nam Theun
Forest Area Laos
WildlifeQueen Elizabeth National
ParkUganda
No patrols
Higher
density
Lower densityx
Yang Ford Brown
59
Uncertainty in Adversary Decision Bounded Rationality
Human Subjects as Poachers
59
Uncertainty in Adversary Decision[2009]
Human subjects Anchoring e-Optimality
-4
-3
-2
-1
0
1
Unobserved 5 Observations 20 Observations Unlimited
Average expected reward
DOBSS
Uniform
COBRA
ARMOR
ARMOR Outperforms uniform random
COBRA
MqxCaq
Xxxts
qxR
ji
Xi
ijj
Xi Qj
jiijqx
)1()()1(
|)|1()1(
max
ee
Anchoring
e-optimality
With Sarit Kraus 44
Pita
5945
Quantal Response Stochastic choice better choice more likely
Quantal Response Model of Adversary [2011] Not Maximize Expected Utility
T
j
jxadversary
EU
jxadversary
EU
j
e
eq
1
))((
))((
-3
-25
-2
-15
-1
-05
0
05
1
15
2
Payoff 1 Payoff 2 Payoff 3 Payoff 4
QuantalResponse
COBRA
ARMOR
Adversaryrsquos probability
of choosing target j
Yang
10
)(max1
1
)(
)(
t tt
defendT
jT
j
jxEU
jxEU
x
xKxts
jxEU
e
e
adversary
adversary
59
Uncertainty in Adversary Decision [2012]
Robust vs Modeling Adversaries
Robustness Bound loss to defender Not model attacker via QR
Defeating Robust Learned subjective utility
Robust wins Draw QR wins
42 52 6
Results on 100 games
SU-QR wins Draw Robust
13 8 1
Results on 22 games
MqxCa
qxR
ji
Xi
ij
jiij
Xi Qj
qx
)1()(0
max
β (Adversaryrsquos utility loss if
deviates from optimal) gt=
(Defenderrsquos utility loss due to
adversary deviation)
penaltyattackw
rewardattackw
probcapturewjaSEU
3
2
1)(
M
j
jxSEU
jxSEU
jadversary
adversary
e
eq
1
)(
)(
SU-QR wins Draw Robust
6 13 3
Results against security experts
With Sarit Kraus 46
5947
PAWS Protection Assistant for Wildlife Security[2014]Repeated Stackelberg Game
Simulation
Learn from crime data
Improve model
Defender calculates
mixed strategy
Defender executes
randomized patrols
Poachers attack targets
119890119878119864119880119894(w1w2w3)
119896 119890119878119864119880119896(w1w2w3)
Bayesian SUQR Heterogeneous Poachers
5948
PAWS Test April 2014
Trials in Queen Elizabeth National Park
Andrew Lemieux with rangers
on PAWS patrol in Uganda
59
Security Resource OptimizationEvaluating Deployed Security Systems Not Easy
Game theory Improvement over previous approaches
Previous Human schedulers or ldquosimple randomrdquo
49
Lab
Evaluation
Simulated
adversary
Human subject
adversaries
Field Evaluation
Patrol quality
Unpredictable Cover
Compare real schedules
Scheduling competition
Expert evaluation
Field Evaluation
Tests against adversaries
ldquoMock attackersrdquo
Capture rates of
real adversaries
59
Why Does Game Theory Perform Better
Weaknesses of Previous Methods
Human schedulers
Predictable patterns eg US Coast Guard
Scheduling effort amp cognitive burden
Simple random (eg dice roll)
Wrong weightscoverage eg officers to sparsely crowded terminals
No adversary reactions
Multiple deployments over multiple years without us forcing them
50
59
Lab Evaluation via Simulations
Example from IRIS (FAMS)
-10
-8
-6
-4
-2
0
2
4
6
50 150 250
Defe
nde
r E
xpe
cte
d u
tilit
y
Schedule Size
Uniform Weighted random 1 Weighted random 2 IRIS
51
59
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage
Patrols Before PROTECT Boston Patrols After PROTECT Boston
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Base Patrol Area
52
PROTECT (Coast Guard) 350 increase defender expected utility
59
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage for Less Effort
53
IRIS for FAMS Outperformed expert human over six monthsReportGAO-09-903T
ARMOR at LAX Savings of up to an hour a day in scheduling
59
Field Evaluation Human vs Game Theory Competition
Counter-terrorism Patrol Scheduling
90 officers on LA Metro Trains
Humans required significant effort
Worse schedules than game theory
Observerrsquos report on questions
54
3
35
4
45
5
55
Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Q9
Q10
Q11
Q12
Secu
rity
Sco
re
Human Game Theory
59
Field Test Against Adversaries Mock Attackers
Example from PROTECT
ldquoMock attackerrdquo team deployed in Boston
Comparing PRE- to POST-PROTECT ldquodeterrencerdquo improved
Additional real-world indicators from Boston
Boston boaters questions
ldquohas the Coast Guard recently acquired more boatsrdquo
POST-PROTECT Actual reports of illegal activity
55
5956
Field Tests Against Adversaries
Computational Game Theory in the Field
Game theory vs Random
21 days of patrol
Identical conditions
Random + Human
Not controlled
0
20
40
60
80
100 Miscellaneous
Drugs
Firearm Violations
0
5
10
15
20
Captures30 min
Warnings30 min
Violations30 min
Game Theory
Rand+Human
Controlled
59
Expert Evaluation
Example from ARMOR IRIS amp PROTECT
February 2009 Commendations
LAX Police (City of Los Angeles)
July 2011 Operational Excellence
Award (US Coast Guard Boston)
September 2011 Certificate of
Appreciation (Federal Air Marshals)
June 2013 Meritorious Team Commendation
from Commandant (US Coast Guard)
57
59
Summary Security Games
Decision aids based on computational game theory in daily use
Optimize limited security resources against adversaries
Applications yield research challenges Science of security games
Scale-up Incremental strategy generation amp Marginals
Uncertainty Integrate MDPs Robustness Quantal response
Current applications (wildlife security) Interdisciplinar challenge
Global challenges Merge planninglearning amp security games
58
59
Just the Beginning of ldquoSecurity Gamesrdquohellip
Our next steps
59
Thank you
to sponsors
StartupARMORWAY Game theory in the field
Follow on research amp applications
Privacy audits
(Sinha IJCAIrsquo13)
Software testing
(Kukreja ASErsquo13)
Sport event security
(Yin AAAIrsquo14)
Singapore train
(Varakantham IAAIrsquo13)
Exam questions
(Li IJCAIrsquo13)
59
THANK YOU
tambeuscedu
httpteamcoreuscedusecurity
60
59
Global Challenges for Security
Game Theory for Security Resource Optimization
2
59
Example Model
Stackelberg Security Games
Security allocation
Targets have weights
Adversary surveillance
Target
1
Target
2
Target 1 4 -3 -1 1
Target 2 -5 5 2 -1
Adversary
3
Defender
59
Example Model
Stackelberg Security Games
Security allocation
Targets have weights
Adversary surveillance
Target
1
Target
2
Target 1 4 -3 -1 1
Target 2 -5 5 2 -1
Adversary
4
Defender
59
Example Model
Stackelberg Security Games
Security allocation
Targets have weights
Adversary surveillance
Target
1
Target
2
Target 1 4 -3 -1 1
Target 2 -5 5 2 -1
Adversary
5
Defender
59
Stackelberg Security GamesSecurity Resource Optimization Not 100 Security
Random strategy
Increase costuncertainty to attackers
Stackelberg game
Defender commits to mixed strategy
Adversary conducts surveillance responds
Stackelberg Equilibrium Optimal random
Target
1
Target
2
Target 1 4 -3 -1 1
Target 2 -5 5 2 -1
Adversary
6
Defender
59
Research Contributions
Game Theory for Security
Computational Game Theory in the Field
Computational game
theory
bull Massive games
Behavioral game
theory
bull Exploit human
behavior
models
+ Planning under uncertainty learninghellip
7
859
Ports amp port traffic
US Coast Guard
Applications Deployed Security Assistants
Airports access
roads amp flights
TSA
Airport Police
Urban transport
LA SheriffrsquosTSA
Singapore Police
Environment
US Coast Guard
WWF WCShellip
59
Key Lessons Security Games
Decision aids based on computational game theory in daily use
Optimize limited security resources against adversaries
Applications yield research challenges Science of security games
Scale-up Incremental strategy generation amp Marginals
Uncertainty Integrate MDPs Robustness Quantal response
Current applications (wildlife security) Interdisciplinary challenge
Global challenges Merge planninglearning amp security games
9
5910
Outline ldquoSecurity Gamesrdquo Research (2007-Now)
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
Evaluation II Real-world deployments
(Patience)
Evaluation I Scale up Handle uncertainty
Publications
AAMAS AAAI IJCAIhellip
2007 onwards
5911
bull 6 plots against LAX
ARMOR LAX (2007) GUARDS TSA (2011)
Airport Security Mapping to Stackelberg Games
GLASGOW 63007
5912
ARMOR Operation [2007]Generate Detailed Defender Schedule Pita Paruchuri
Mixed Integer Program
Pr(Canine patrol 8 AM Terminals 357) = 033
Pr(Canine patrol 8 AM Terminals 256) = 017
helliphellipCanine Team Schedule July 28Term 1 Term 2 Term 3 Term 4 Term 5 Term 6 Term 7 Term 8
8 AM Team1 Team3 Team5
9 AM Team1 Team2 Team4
10 AM Team3 Team5 Team2
Target 1 Target 2
Defender 1 2 -1 -3 4
Defender 2 -3 3 3 -2
5913
ARMOR MIP [2007]Generate Mixed Strategy for Defender
1 i
ixts
jq
ix
ijR
Xi Qj
max
1Qj
jq
MqxCa ji
Xi
ij )1()(0
Maximize defender
expected utility
Defender mixed
strategy
Adversary best
response
Pita Paruchuri
Target 1 Target 2
Defender 1 2 -1 -3 4
Defender 2 -3 3 3 -2
Adversary response
5914
ARMOR Payoffs [2007]Previous Research Provides Payoffs in Security Game Domains
Target 1 Target 2
Defender 1 2 -1 -3 4
Defender 2 -3 3 3 -2
jq
ix
ijR
Xi Qj
maxMaximize defender
expected utility
5915
ARMOR MIP [2007]Solving for a Single Adversary Type
Term 1 Term 2
Defend1 2 -1 -3 4
Defend2 -3 1 3 -3
1 i
ixts
jq
ix
ijR
Xi Qj
max
1Qj
jq
ljq][ix
MqxCa ji
Xi
ij
1010
)1()(0
Maximize defender
expected utility
Defender strategy
Adversary strategy
Adversary best
response
119909119894
ARMORhellipthrows a digital cloak of invisibilityhellip
59
IRIS Federal Air Marshals Service [2009]Scale Up Number of Defender Strategies
1000 Flights 20 air marshals 1041
combinations
ARMOR out of memory
Strategy 1 Strategy 2 Strategy 3
Strateg
y 1
Strateg
y 2
Strateg
y 3
Strateg
y 4
Strateg
y 5
Strateg
y 6
Not enumerate all combinations
Branch and price Incremental strategy generation
Strategy 1 Strategy 2 Strategy 3
Strategy 1
Strategy 2
Strategy 3
Strategy 4
Strategy 5
Strategy 6
16
59
IRIS Scale Up Number of Defender Strategies [2009]
Small Support Set for Mixed Strategies
Small support set size
bull Most xi variables zero
x124=0239
x123=00
x135=00
Attack
1
Attack
2
Attack
hellip
Attack
1000
123 5-10 4-8 hellip -209
124 5-10 4-8 hellip -209
135 5-10 -95 hellip -209
hellip
hellip1041 rowsx378=0123
10]10[
)1()(0
11
max
jqx
MqxCa
qxts
qxR
i
ji
Xi
ij
Qj
j
i
i
jiij
Xi Qj
qx
1000 flights 20 air marshals
1041combinations
17
59
Target 3 Target 7
hellip hellip
Resource Sink
Best new pure strategy
Minimum cost network flow
IRIS Incremental Strategy Generation
Exploit Small Support
Attack 1 Attack 2 Attack Attack 6
124 5-10 4-8 hellip -209
378 -8 10 -810 hellip -810
hellip
500 rows
NOT 1041
Attack 1 Attack 2 Attackhellip Attack 6
124 5-10 4-8 hellip -209
Slave (LP Duality Theory)
Master
Converge
GLOBAL
OPTIMAL
Attack 1 Attack 2 Attackhellip Attack 6
124 5-10 4-8 hellip -209
378 -8 10 -810 hellip -810
18
Jain Kiekintveld
59
IRIS Deployed FAMS (2009-)
ldquohellipin 2011 the Military Operations Research Society selected a University
of Southern California project with FAMS on randomizing flight
schedules for the prestigious Rist Awardhelliprdquo
-R S Bray (TSA)
Transportation Security Subcommittee
US House of Representatives 2012
19
Significant change in operations
59
Networks Mumbai Police Checkpoints[2013]
150 edges 2 Checkpoints
150-choose-2 strategies
With V Conitzer 20
Jain
59
Networks Mumbai Police Checkpoints[2013]
Incremental Strategy Generation
With V Conitzer 21
Jain
Double oracle Converge to a global optimal
Path 1 Path 2
Checkpoint
strategy 15 -5 -1 1
Path 1 Path 2
Checkpoint
strategy 15 -5 -1 1
Checkpoint
strategy 2-5 5 2 -1
Defender oracle
Path 1 Path 2 Path 3
Checkpoint
strategy 15 -5 -1 1 -2 2
Checkpoint
strategy 2-5 5 1 -1 -2 2
Attacker oracle
Path 1 Path 2 Path 3
Checkpoint
strategy 15 -5 -1 1 -2 2
Checkpoint
strategy 2-5 5 1 -1 -2 2
59
Double Oracle[2013]Incremental Strategy Generation Exploit Small Support
22
150 edges 2 Checkpoints
Only six candidate edges
for checkpoints
Jain
5923
Mumbai Police Checkpoints[2013]Results of Scale-up
20416 Roads15 checkpoints
20 min
59
Double Oracle Social Cyber Networks[2013]
Incremental Strategy Generation
24
Social networkseg counter-insurgency
Sources
Intermediate
Nodes
Links
Targets
Cyber networks
Tsai
59
Outline ldquoSecurity Gamesrdquo Research
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
25
59
Port Security Threat Scenarios
US Ports $315 trillion economy
USS Cole after suicide attack
French oil tanker hit by small boat
Attack on a ferry
26
59
PROTECT Randomized Patrol Scheduling [2013]
27
Port Protection (Scale-up) and Ferries (Continuous Spacetime)
59
PROTECT Randomized Patrol Scheduling [2013]Port Protection (Scale-up) and Ferries (Continuous Spacetime)
28
59
Ferries Scale-up with Mobile Resources amp Moving TargetsTransition Graph Representation
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
29
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Ferries Scale-up with Mobile Resources amp Moving TargetsTransition Graph Representation
30
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
31
Patrols protect nearby ferry location Solve as done in ARMOR
Ferries Patrol RoutesExponential Numbers of Patrol Routes
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Patrols protect nearby ferry location Solve as done in ARMOR
Ferries Patrol RoutesExponential Numbers of Patrol Routes
32
Pr([(B5) (C 10) (C15)]) = 017
Pr([(B5) (C10) (B15)]) =013
NT variables
Pr([(A5) (A10) (B15)]) = 007
Pr([(A5) (A10) (A15)]) = 003
Jiang KiekintveldFang
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Variables NOT routes but probability flow over each segment
Ferries Scale-up Marginal Probabilities Over Segments
33
NT variables
Jiang KiekintveldFang
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
030
017
013
Obtain marginal probabilities over segments
010
007
003
N2T variablesExtract Pr([(B5) (C 10) (C15)]) = 017
Pr([(B5) (C10) (B15)]) =013
Ferries Scale-up with Marginals Over Separable SegmentsSignificant Speedup
34
NT variables
59
5936
Outline ldquoSecurity Gamesrdquo Research (2007-Now)
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
Evaluation II Real-world deployments
(Patience)
Evaluation I Scale up Handle uncertainty
59
TRUSTS Frequent adversary interaction games
Patrols Against Fare Evaders
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
017
013
010
010
37
59
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
38
Jiang Delle Fave
59
Markov Decision Problems in Security games
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
39
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution Jiang Delle Fave
5940
Urban Transportation Security
MDPs amp DEC-MDPs in Security Games
COPS LA Metro System(Against Opportunistic Crime)
STREETS Singapore Roads(Against Reckless Driving)
DEC-MDPs in Security Games Teamwork + Uncertainty
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
ZhangShieh
59
Uncertainty Space Algorithms
Bayesian and Robust Approaches
Adversary payoff uncertainty
Adversary observation amp
defender execution
uncertainty
Adversary rationality
uncertainty
RECON
BRASS
Monotonic Maximin
(Monotonic adversary)
41
URAC
Payoff interval
Not point
estimateGMC HUNTER
Bayesian
Robust
Yin Nguyen An
0
05
1
15
5 10
Def
end
ers
EU
Targets
ISG RECON URAC
59
Security Games Environmental Crime
amp Bounded Rationality
FisheryGulf of Mexico
Nakai Nam Theun
Forest Area Laos
WildlifeQueen Elizabeth National
ParkUganda
No patrols
Higher
density
Lower densityx
Yang Ford Brown
59
Uncertainty in Adversary Decision Bounded Rationality
Human Subjects as Poachers
59
Uncertainty in Adversary Decision[2009]
Human subjects Anchoring e-Optimality
-4
-3
-2
-1
0
1
Unobserved 5 Observations 20 Observations Unlimited
Average expected reward
DOBSS
Uniform
COBRA
ARMOR
ARMOR Outperforms uniform random
COBRA
MqxCaq
Xxxts
qxR
ji
Xi
ijj
Xi Qj
jiijqx
)1()()1(
|)|1()1(
max
ee
Anchoring
e-optimality
With Sarit Kraus 44
Pita
5945
Quantal Response Stochastic choice better choice more likely
Quantal Response Model of Adversary [2011] Not Maximize Expected Utility
T
j
jxadversary
EU
jxadversary
EU
j
e
eq
1
))((
))((
-3
-25
-2
-15
-1
-05
0
05
1
15
2
Payoff 1 Payoff 2 Payoff 3 Payoff 4
QuantalResponse
COBRA
ARMOR
Adversaryrsquos probability
of choosing target j
Yang
10
)(max1
1
)(
)(
t tt
defendT
jT
j
jxEU
jxEU
x
xKxts
jxEU
e
e
adversary
adversary
59
Uncertainty in Adversary Decision [2012]
Robust vs Modeling Adversaries
Robustness Bound loss to defender Not model attacker via QR
Defeating Robust Learned subjective utility
Robust wins Draw QR wins
42 52 6
Results on 100 games
SU-QR wins Draw Robust
13 8 1
Results on 22 games
MqxCa
qxR
ji
Xi
ij
jiij
Xi Qj
qx
)1()(0
max
β (Adversaryrsquos utility loss if
deviates from optimal) gt=
(Defenderrsquos utility loss due to
adversary deviation)
penaltyattackw
rewardattackw
probcapturewjaSEU
3
2
1)(
M
j
jxSEU
jxSEU
jadversary
adversary
e
eq
1
)(
)(
SU-QR wins Draw Robust
6 13 3
Results against security experts
With Sarit Kraus 46
5947
PAWS Protection Assistant for Wildlife Security[2014]Repeated Stackelberg Game
Simulation
Learn from crime data
Improve model
Defender calculates
mixed strategy
Defender executes
randomized patrols
Poachers attack targets
119890119878119864119880119894(w1w2w3)
119896 119890119878119864119880119896(w1w2w3)
Bayesian SUQR Heterogeneous Poachers
5948
PAWS Test April 2014
Trials in Queen Elizabeth National Park
Andrew Lemieux with rangers
on PAWS patrol in Uganda
59
Security Resource OptimizationEvaluating Deployed Security Systems Not Easy
Game theory Improvement over previous approaches
Previous Human schedulers or ldquosimple randomrdquo
49
Lab
Evaluation
Simulated
adversary
Human subject
adversaries
Field Evaluation
Patrol quality
Unpredictable Cover
Compare real schedules
Scheduling competition
Expert evaluation
Field Evaluation
Tests against adversaries
ldquoMock attackersrdquo
Capture rates of
real adversaries
59
Why Does Game Theory Perform Better
Weaknesses of Previous Methods
Human schedulers
Predictable patterns eg US Coast Guard
Scheduling effort amp cognitive burden
Simple random (eg dice roll)
Wrong weightscoverage eg officers to sparsely crowded terminals
No adversary reactions
Multiple deployments over multiple years without us forcing them
50
59
Lab Evaluation via Simulations
Example from IRIS (FAMS)
-10
-8
-6
-4
-2
0
2
4
6
50 150 250
Defe
nde
r E
xpe
cte
d u
tilit
y
Schedule Size
Uniform Weighted random 1 Weighted random 2 IRIS
51
59
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage
Patrols Before PROTECT Boston Patrols After PROTECT Boston
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Base Patrol Area
52
PROTECT (Coast Guard) 350 increase defender expected utility
59
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage for Less Effort
53
IRIS for FAMS Outperformed expert human over six monthsReportGAO-09-903T
ARMOR at LAX Savings of up to an hour a day in scheduling
59
Field Evaluation Human vs Game Theory Competition
Counter-terrorism Patrol Scheduling
90 officers on LA Metro Trains
Humans required significant effort
Worse schedules than game theory
Observerrsquos report on questions
54
3
35
4
45
5
55
Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Q9
Q10
Q11
Q12
Secu
rity
Sco
re
Human Game Theory
59
Field Test Against Adversaries Mock Attackers
Example from PROTECT
ldquoMock attackerrdquo team deployed in Boston
Comparing PRE- to POST-PROTECT ldquodeterrencerdquo improved
Additional real-world indicators from Boston
Boston boaters questions
ldquohas the Coast Guard recently acquired more boatsrdquo
POST-PROTECT Actual reports of illegal activity
55
5956
Field Tests Against Adversaries
Computational Game Theory in the Field
Game theory vs Random
21 days of patrol
Identical conditions
Random + Human
Not controlled
0
20
40
60
80
100 Miscellaneous
Drugs
Firearm Violations
0
5
10
15
20
Captures30 min
Warnings30 min
Violations30 min
Game Theory
Rand+Human
Controlled
59
Expert Evaluation
Example from ARMOR IRIS amp PROTECT
February 2009 Commendations
LAX Police (City of Los Angeles)
July 2011 Operational Excellence
Award (US Coast Guard Boston)
September 2011 Certificate of
Appreciation (Federal Air Marshals)
June 2013 Meritorious Team Commendation
from Commandant (US Coast Guard)
57
59
Summary Security Games
Decision aids based on computational game theory in daily use
Optimize limited security resources against adversaries
Applications yield research challenges Science of security games
Scale-up Incremental strategy generation amp Marginals
Uncertainty Integrate MDPs Robustness Quantal response
Current applications (wildlife security) Interdisciplinar challenge
Global challenges Merge planninglearning amp security games
58
59
Just the Beginning of ldquoSecurity Gamesrdquohellip
Our next steps
59
Thank you
to sponsors
StartupARMORWAY Game theory in the field
Follow on research amp applications
Privacy audits
(Sinha IJCAIrsquo13)
Software testing
(Kukreja ASErsquo13)
Sport event security
(Yin AAAIrsquo14)
Singapore train
(Varakantham IAAIrsquo13)
Exam questions
(Li IJCAIrsquo13)
59
THANK YOU
tambeuscedu
httpteamcoreuscedusecurity
60
59
Example Model
Stackelberg Security Games
Security allocation
Targets have weights
Adversary surveillance
Target
1
Target
2
Target 1 4 -3 -1 1
Target 2 -5 5 2 -1
Adversary
3
Defender
59
Example Model
Stackelberg Security Games
Security allocation
Targets have weights
Adversary surveillance
Target
1
Target
2
Target 1 4 -3 -1 1
Target 2 -5 5 2 -1
Adversary
4
Defender
59
Example Model
Stackelberg Security Games
Security allocation
Targets have weights
Adversary surveillance
Target
1
Target
2
Target 1 4 -3 -1 1
Target 2 -5 5 2 -1
Adversary
5
Defender
59
Stackelberg Security GamesSecurity Resource Optimization Not 100 Security
Random strategy
Increase costuncertainty to attackers
Stackelberg game
Defender commits to mixed strategy
Adversary conducts surveillance responds
Stackelberg Equilibrium Optimal random
Target
1
Target
2
Target 1 4 -3 -1 1
Target 2 -5 5 2 -1
Adversary
6
Defender
59
Research Contributions
Game Theory for Security
Computational Game Theory in the Field
Computational game
theory
bull Massive games
Behavioral game
theory
bull Exploit human
behavior
models
+ Planning under uncertainty learninghellip
7
859
Ports amp port traffic
US Coast Guard
Applications Deployed Security Assistants
Airports access
roads amp flights
TSA
Airport Police
Urban transport
LA SheriffrsquosTSA
Singapore Police
Environment
US Coast Guard
WWF WCShellip
59
Key Lessons Security Games
Decision aids based on computational game theory in daily use
Optimize limited security resources against adversaries
Applications yield research challenges Science of security games
Scale-up Incremental strategy generation amp Marginals
Uncertainty Integrate MDPs Robustness Quantal response
Current applications (wildlife security) Interdisciplinary challenge
Global challenges Merge planninglearning amp security games
9
5910
Outline ldquoSecurity Gamesrdquo Research (2007-Now)
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
Evaluation II Real-world deployments
(Patience)
Evaluation I Scale up Handle uncertainty
Publications
AAMAS AAAI IJCAIhellip
2007 onwards
5911
bull 6 plots against LAX
ARMOR LAX (2007) GUARDS TSA (2011)
Airport Security Mapping to Stackelberg Games
GLASGOW 63007
5912
ARMOR Operation [2007]Generate Detailed Defender Schedule Pita Paruchuri
Mixed Integer Program
Pr(Canine patrol 8 AM Terminals 357) = 033
Pr(Canine patrol 8 AM Terminals 256) = 017
helliphellipCanine Team Schedule July 28Term 1 Term 2 Term 3 Term 4 Term 5 Term 6 Term 7 Term 8
8 AM Team1 Team3 Team5
9 AM Team1 Team2 Team4
10 AM Team3 Team5 Team2
Target 1 Target 2
Defender 1 2 -1 -3 4
Defender 2 -3 3 3 -2
5913
ARMOR MIP [2007]Generate Mixed Strategy for Defender
1 i
ixts
jq
ix
ijR
Xi Qj
max
1Qj
jq
MqxCa ji
Xi
ij )1()(0
Maximize defender
expected utility
Defender mixed
strategy
Adversary best
response
Pita Paruchuri
Target 1 Target 2
Defender 1 2 -1 -3 4
Defender 2 -3 3 3 -2
Adversary response
5914
ARMOR Payoffs [2007]Previous Research Provides Payoffs in Security Game Domains
Target 1 Target 2
Defender 1 2 -1 -3 4
Defender 2 -3 3 3 -2
jq
ix
ijR
Xi Qj
maxMaximize defender
expected utility
5915
ARMOR MIP [2007]Solving for a Single Adversary Type
Term 1 Term 2
Defend1 2 -1 -3 4
Defend2 -3 1 3 -3
1 i
ixts
jq
ix
ijR
Xi Qj
max
1Qj
jq
ljq][ix
MqxCa ji
Xi
ij
1010
)1()(0
Maximize defender
expected utility
Defender strategy
Adversary strategy
Adversary best
response
119909119894
ARMORhellipthrows a digital cloak of invisibilityhellip
59
IRIS Federal Air Marshals Service [2009]Scale Up Number of Defender Strategies
1000 Flights 20 air marshals 1041
combinations
ARMOR out of memory
Strategy 1 Strategy 2 Strategy 3
Strateg
y 1
Strateg
y 2
Strateg
y 3
Strateg
y 4
Strateg
y 5
Strateg
y 6
Not enumerate all combinations
Branch and price Incremental strategy generation
Strategy 1 Strategy 2 Strategy 3
Strategy 1
Strategy 2
Strategy 3
Strategy 4
Strategy 5
Strategy 6
16
59
IRIS Scale Up Number of Defender Strategies [2009]
Small Support Set for Mixed Strategies
Small support set size
bull Most xi variables zero
x124=0239
x123=00
x135=00
Attack
1
Attack
2
Attack
hellip
Attack
1000
123 5-10 4-8 hellip -209
124 5-10 4-8 hellip -209
135 5-10 -95 hellip -209
hellip
hellip1041 rowsx378=0123
10]10[
)1()(0
11
max
jqx
MqxCa
qxts
qxR
i
ji
Xi
ij
Qj
j
i
i
jiij
Xi Qj
qx
1000 flights 20 air marshals
1041combinations
17
59
Target 3 Target 7
hellip hellip
Resource Sink
Best new pure strategy
Minimum cost network flow
IRIS Incremental Strategy Generation
Exploit Small Support
Attack 1 Attack 2 Attack Attack 6
124 5-10 4-8 hellip -209
378 -8 10 -810 hellip -810
hellip
500 rows
NOT 1041
Attack 1 Attack 2 Attackhellip Attack 6
124 5-10 4-8 hellip -209
Slave (LP Duality Theory)
Master
Converge
GLOBAL
OPTIMAL
Attack 1 Attack 2 Attackhellip Attack 6
124 5-10 4-8 hellip -209
378 -8 10 -810 hellip -810
18
Jain Kiekintveld
59
IRIS Deployed FAMS (2009-)
ldquohellipin 2011 the Military Operations Research Society selected a University
of Southern California project with FAMS on randomizing flight
schedules for the prestigious Rist Awardhelliprdquo
-R S Bray (TSA)
Transportation Security Subcommittee
US House of Representatives 2012
19
Significant change in operations
59
Networks Mumbai Police Checkpoints[2013]
150 edges 2 Checkpoints
150-choose-2 strategies
With V Conitzer 20
Jain
59
Networks Mumbai Police Checkpoints[2013]
Incremental Strategy Generation
With V Conitzer 21
Jain
Double oracle Converge to a global optimal
Path 1 Path 2
Checkpoint
strategy 15 -5 -1 1
Path 1 Path 2
Checkpoint
strategy 15 -5 -1 1
Checkpoint
strategy 2-5 5 2 -1
Defender oracle
Path 1 Path 2 Path 3
Checkpoint
strategy 15 -5 -1 1 -2 2
Checkpoint
strategy 2-5 5 1 -1 -2 2
Attacker oracle
Path 1 Path 2 Path 3
Checkpoint
strategy 15 -5 -1 1 -2 2
Checkpoint
strategy 2-5 5 1 -1 -2 2
59
Double Oracle[2013]Incremental Strategy Generation Exploit Small Support
22
150 edges 2 Checkpoints
Only six candidate edges
for checkpoints
Jain
5923
Mumbai Police Checkpoints[2013]Results of Scale-up
20416 Roads15 checkpoints
20 min
59
Double Oracle Social Cyber Networks[2013]
Incremental Strategy Generation
24
Social networkseg counter-insurgency
Sources
Intermediate
Nodes
Links
Targets
Cyber networks
Tsai
59
Outline ldquoSecurity Gamesrdquo Research
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
25
59
Port Security Threat Scenarios
US Ports $315 trillion economy
USS Cole after suicide attack
French oil tanker hit by small boat
Attack on a ferry
26
59
PROTECT Randomized Patrol Scheduling [2013]
27
Port Protection (Scale-up) and Ferries (Continuous Spacetime)
59
PROTECT Randomized Patrol Scheduling [2013]Port Protection (Scale-up) and Ferries (Continuous Spacetime)
28
59
Ferries Scale-up with Mobile Resources amp Moving TargetsTransition Graph Representation
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
29
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Ferries Scale-up with Mobile Resources amp Moving TargetsTransition Graph Representation
30
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
31
Patrols protect nearby ferry location Solve as done in ARMOR
Ferries Patrol RoutesExponential Numbers of Patrol Routes
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Patrols protect nearby ferry location Solve as done in ARMOR
Ferries Patrol RoutesExponential Numbers of Patrol Routes
32
Pr([(B5) (C 10) (C15)]) = 017
Pr([(B5) (C10) (B15)]) =013
NT variables
Pr([(A5) (A10) (B15)]) = 007
Pr([(A5) (A10) (A15)]) = 003
Jiang KiekintveldFang
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Variables NOT routes but probability flow over each segment
Ferries Scale-up Marginal Probabilities Over Segments
33
NT variables
Jiang KiekintveldFang
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
030
017
013
Obtain marginal probabilities over segments
010
007
003
N2T variablesExtract Pr([(B5) (C 10) (C15)]) = 017
Pr([(B5) (C10) (B15)]) =013
Ferries Scale-up with Marginals Over Separable SegmentsSignificant Speedup
34
NT variables
59
5936
Outline ldquoSecurity Gamesrdquo Research (2007-Now)
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
Evaluation II Real-world deployments
(Patience)
Evaluation I Scale up Handle uncertainty
59
TRUSTS Frequent adversary interaction games
Patrols Against Fare Evaders
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
017
013
010
010
37
59
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
38
Jiang Delle Fave
59
Markov Decision Problems in Security games
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
39
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution Jiang Delle Fave
5940
Urban Transportation Security
MDPs amp DEC-MDPs in Security Games
COPS LA Metro System(Against Opportunistic Crime)
STREETS Singapore Roads(Against Reckless Driving)
DEC-MDPs in Security Games Teamwork + Uncertainty
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
ZhangShieh
59
Uncertainty Space Algorithms
Bayesian and Robust Approaches
Adversary payoff uncertainty
Adversary observation amp
defender execution
uncertainty
Adversary rationality
uncertainty
RECON
BRASS
Monotonic Maximin
(Monotonic adversary)
41
URAC
Payoff interval
Not point
estimateGMC HUNTER
Bayesian
Robust
Yin Nguyen An
0
05
1
15
5 10
Def
end
ers
EU
Targets
ISG RECON URAC
59
Security Games Environmental Crime
amp Bounded Rationality
FisheryGulf of Mexico
Nakai Nam Theun
Forest Area Laos
WildlifeQueen Elizabeth National
ParkUganda
No patrols
Higher
density
Lower densityx
Yang Ford Brown
59
Uncertainty in Adversary Decision Bounded Rationality
Human Subjects as Poachers
59
Uncertainty in Adversary Decision[2009]
Human subjects Anchoring e-Optimality
-4
-3
-2
-1
0
1
Unobserved 5 Observations 20 Observations Unlimited
Average expected reward
DOBSS
Uniform
COBRA
ARMOR
ARMOR Outperforms uniform random
COBRA
MqxCaq
Xxxts
qxR
ji
Xi
ijj
Xi Qj
jiijqx
)1()()1(
|)|1()1(
max
ee
Anchoring
e-optimality
With Sarit Kraus 44
Pita
5945
Quantal Response Stochastic choice better choice more likely
Quantal Response Model of Adversary [2011] Not Maximize Expected Utility
T
j
jxadversary
EU
jxadversary
EU
j
e
eq
1
))((
))((
-3
-25
-2
-15
-1
-05
0
05
1
15
2
Payoff 1 Payoff 2 Payoff 3 Payoff 4
QuantalResponse
COBRA
ARMOR
Adversaryrsquos probability
of choosing target j
Yang
10
)(max1
1
)(
)(
t tt
defendT
jT
j
jxEU
jxEU
x
xKxts
jxEU
e
e
adversary
adversary
59
Uncertainty in Adversary Decision [2012]
Robust vs Modeling Adversaries
Robustness Bound loss to defender Not model attacker via QR
Defeating Robust Learned subjective utility
Robust wins Draw QR wins
42 52 6
Results on 100 games
SU-QR wins Draw Robust
13 8 1
Results on 22 games
MqxCa
qxR
ji
Xi
ij
jiij
Xi Qj
qx
)1()(0
max
β (Adversaryrsquos utility loss if
deviates from optimal) gt=
(Defenderrsquos utility loss due to
adversary deviation)
penaltyattackw
rewardattackw
probcapturewjaSEU
3
2
1)(
M
j
jxSEU
jxSEU
jadversary
adversary
e
eq
1
)(
)(
SU-QR wins Draw Robust
6 13 3
Results against security experts
With Sarit Kraus 46
5947
PAWS Protection Assistant for Wildlife Security[2014]Repeated Stackelberg Game
Simulation
Learn from crime data
Improve model
Defender calculates
mixed strategy
Defender executes
randomized patrols
Poachers attack targets
119890119878119864119880119894(w1w2w3)
119896 119890119878119864119880119896(w1w2w3)
Bayesian SUQR Heterogeneous Poachers
5948
PAWS Test April 2014
Trials in Queen Elizabeth National Park
Andrew Lemieux with rangers
on PAWS patrol in Uganda
59
Security Resource OptimizationEvaluating Deployed Security Systems Not Easy
Game theory Improvement over previous approaches
Previous Human schedulers or ldquosimple randomrdquo
49
Lab
Evaluation
Simulated
adversary
Human subject
adversaries
Field Evaluation
Patrol quality
Unpredictable Cover
Compare real schedules
Scheduling competition
Expert evaluation
Field Evaluation
Tests against adversaries
ldquoMock attackersrdquo
Capture rates of
real adversaries
59
Why Does Game Theory Perform Better
Weaknesses of Previous Methods
Human schedulers
Predictable patterns eg US Coast Guard
Scheduling effort amp cognitive burden
Simple random (eg dice roll)
Wrong weightscoverage eg officers to sparsely crowded terminals
No adversary reactions
Multiple deployments over multiple years without us forcing them
50
59
Lab Evaluation via Simulations
Example from IRIS (FAMS)
-10
-8
-6
-4
-2
0
2
4
6
50 150 250
Defe
nde
r E
xpe
cte
d u
tilit
y
Schedule Size
Uniform Weighted random 1 Weighted random 2 IRIS
51
59
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage
Patrols Before PROTECT Boston Patrols After PROTECT Boston
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Base Patrol Area
52
PROTECT (Coast Guard) 350 increase defender expected utility
59
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage for Less Effort
53
IRIS for FAMS Outperformed expert human over six monthsReportGAO-09-903T
ARMOR at LAX Savings of up to an hour a day in scheduling
59
Field Evaluation Human vs Game Theory Competition
Counter-terrorism Patrol Scheduling
90 officers on LA Metro Trains
Humans required significant effort
Worse schedules than game theory
Observerrsquos report on questions
54
3
35
4
45
5
55
Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Q9
Q10
Q11
Q12
Secu
rity
Sco
re
Human Game Theory
59
Field Test Against Adversaries Mock Attackers
Example from PROTECT
ldquoMock attackerrdquo team deployed in Boston
Comparing PRE- to POST-PROTECT ldquodeterrencerdquo improved
Additional real-world indicators from Boston
Boston boaters questions
ldquohas the Coast Guard recently acquired more boatsrdquo
POST-PROTECT Actual reports of illegal activity
55
5956
Field Tests Against Adversaries
Computational Game Theory in the Field
Game theory vs Random
21 days of patrol
Identical conditions
Random + Human
Not controlled
0
20
40
60
80
100 Miscellaneous
Drugs
Firearm Violations
0
5
10
15
20
Captures30 min
Warnings30 min
Violations30 min
Game Theory
Rand+Human
Controlled
59
Expert Evaluation
Example from ARMOR IRIS amp PROTECT
February 2009 Commendations
LAX Police (City of Los Angeles)
July 2011 Operational Excellence
Award (US Coast Guard Boston)
September 2011 Certificate of
Appreciation (Federal Air Marshals)
June 2013 Meritorious Team Commendation
from Commandant (US Coast Guard)
57
59
Summary Security Games
Decision aids based on computational game theory in daily use
Optimize limited security resources against adversaries
Applications yield research challenges Science of security games
Scale-up Incremental strategy generation amp Marginals
Uncertainty Integrate MDPs Robustness Quantal response
Current applications (wildlife security) Interdisciplinar challenge
Global challenges Merge planninglearning amp security games
58
59
Just the Beginning of ldquoSecurity Gamesrdquohellip
Our next steps
59
Thank you
to sponsors
StartupARMORWAY Game theory in the field
Follow on research amp applications
Privacy audits
(Sinha IJCAIrsquo13)
Software testing
(Kukreja ASErsquo13)
Sport event security
(Yin AAAIrsquo14)
Singapore train
(Varakantham IAAIrsquo13)
Exam questions
(Li IJCAIrsquo13)
59
THANK YOU
tambeuscedu
httpteamcoreuscedusecurity
60
59
Example Model
Stackelberg Security Games
Security allocation
Targets have weights
Adversary surveillance
Target
1
Target
2
Target 1 4 -3 -1 1
Target 2 -5 5 2 -1
Adversary
4
Defender
59
Example Model
Stackelberg Security Games
Security allocation
Targets have weights
Adversary surveillance
Target
1
Target
2
Target 1 4 -3 -1 1
Target 2 -5 5 2 -1
Adversary
5
Defender
59
Stackelberg Security GamesSecurity Resource Optimization Not 100 Security
Random strategy
Increase costuncertainty to attackers
Stackelberg game
Defender commits to mixed strategy
Adversary conducts surveillance responds
Stackelberg Equilibrium Optimal random
Target
1
Target
2
Target 1 4 -3 -1 1
Target 2 -5 5 2 -1
Adversary
6
Defender
59
Research Contributions
Game Theory for Security
Computational Game Theory in the Field
Computational game
theory
bull Massive games
Behavioral game
theory
bull Exploit human
behavior
models
+ Planning under uncertainty learninghellip
7
859
Ports amp port traffic
US Coast Guard
Applications Deployed Security Assistants
Airports access
roads amp flights
TSA
Airport Police
Urban transport
LA SheriffrsquosTSA
Singapore Police
Environment
US Coast Guard
WWF WCShellip
59
Key Lessons Security Games
Decision aids based on computational game theory in daily use
Optimize limited security resources against adversaries
Applications yield research challenges Science of security games
Scale-up Incremental strategy generation amp Marginals
Uncertainty Integrate MDPs Robustness Quantal response
Current applications (wildlife security) Interdisciplinary challenge
Global challenges Merge planninglearning amp security games
9
5910
Outline ldquoSecurity Gamesrdquo Research (2007-Now)
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
Evaluation II Real-world deployments
(Patience)
Evaluation I Scale up Handle uncertainty
Publications
AAMAS AAAI IJCAIhellip
2007 onwards
5911
bull 6 plots against LAX
ARMOR LAX (2007) GUARDS TSA (2011)
Airport Security Mapping to Stackelberg Games
GLASGOW 63007
5912
ARMOR Operation [2007]Generate Detailed Defender Schedule Pita Paruchuri
Mixed Integer Program
Pr(Canine patrol 8 AM Terminals 357) = 033
Pr(Canine patrol 8 AM Terminals 256) = 017
helliphellipCanine Team Schedule July 28Term 1 Term 2 Term 3 Term 4 Term 5 Term 6 Term 7 Term 8
8 AM Team1 Team3 Team5
9 AM Team1 Team2 Team4
10 AM Team3 Team5 Team2
Target 1 Target 2
Defender 1 2 -1 -3 4
Defender 2 -3 3 3 -2
5913
ARMOR MIP [2007]Generate Mixed Strategy for Defender
1 i
ixts
jq
ix
ijR
Xi Qj
max
1Qj
jq
MqxCa ji
Xi
ij )1()(0
Maximize defender
expected utility
Defender mixed
strategy
Adversary best
response
Pita Paruchuri
Target 1 Target 2
Defender 1 2 -1 -3 4
Defender 2 -3 3 3 -2
Adversary response
5914
ARMOR Payoffs [2007]Previous Research Provides Payoffs in Security Game Domains
Target 1 Target 2
Defender 1 2 -1 -3 4
Defender 2 -3 3 3 -2
jq
ix
ijR
Xi Qj
maxMaximize defender
expected utility
5915
ARMOR MIP [2007]Solving for a Single Adversary Type
Term 1 Term 2
Defend1 2 -1 -3 4
Defend2 -3 1 3 -3
1 i
ixts
jq
ix
ijR
Xi Qj
max
1Qj
jq
ljq][ix
MqxCa ji
Xi
ij
1010
)1()(0
Maximize defender
expected utility
Defender strategy
Adversary strategy
Adversary best
response
119909119894
ARMORhellipthrows a digital cloak of invisibilityhellip
59
IRIS Federal Air Marshals Service [2009]Scale Up Number of Defender Strategies
1000 Flights 20 air marshals 1041
combinations
ARMOR out of memory
Strategy 1 Strategy 2 Strategy 3
Strateg
y 1
Strateg
y 2
Strateg
y 3
Strateg
y 4
Strateg
y 5
Strateg
y 6
Not enumerate all combinations
Branch and price Incremental strategy generation
Strategy 1 Strategy 2 Strategy 3
Strategy 1
Strategy 2
Strategy 3
Strategy 4
Strategy 5
Strategy 6
16
59
IRIS Scale Up Number of Defender Strategies [2009]
Small Support Set for Mixed Strategies
Small support set size
bull Most xi variables zero
x124=0239
x123=00
x135=00
Attack
1
Attack
2
Attack
hellip
Attack
1000
123 5-10 4-8 hellip -209
124 5-10 4-8 hellip -209
135 5-10 -95 hellip -209
hellip
hellip1041 rowsx378=0123
10]10[
)1()(0
11
max
jqx
MqxCa
qxts
qxR
i
ji
Xi
ij
Qj
j
i
i
jiij
Xi Qj
qx
1000 flights 20 air marshals
1041combinations
17
59
Target 3 Target 7
hellip hellip
Resource Sink
Best new pure strategy
Minimum cost network flow
IRIS Incremental Strategy Generation
Exploit Small Support
Attack 1 Attack 2 Attack Attack 6
124 5-10 4-8 hellip -209
378 -8 10 -810 hellip -810
hellip
500 rows
NOT 1041
Attack 1 Attack 2 Attackhellip Attack 6
124 5-10 4-8 hellip -209
Slave (LP Duality Theory)
Master
Converge
GLOBAL
OPTIMAL
Attack 1 Attack 2 Attackhellip Attack 6
124 5-10 4-8 hellip -209
378 -8 10 -810 hellip -810
18
Jain Kiekintveld
59
IRIS Deployed FAMS (2009-)
ldquohellipin 2011 the Military Operations Research Society selected a University
of Southern California project with FAMS on randomizing flight
schedules for the prestigious Rist Awardhelliprdquo
-R S Bray (TSA)
Transportation Security Subcommittee
US House of Representatives 2012
19
Significant change in operations
59
Networks Mumbai Police Checkpoints[2013]
150 edges 2 Checkpoints
150-choose-2 strategies
With V Conitzer 20
Jain
59
Networks Mumbai Police Checkpoints[2013]
Incremental Strategy Generation
With V Conitzer 21
Jain
Double oracle Converge to a global optimal
Path 1 Path 2
Checkpoint
strategy 15 -5 -1 1
Path 1 Path 2
Checkpoint
strategy 15 -5 -1 1
Checkpoint
strategy 2-5 5 2 -1
Defender oracle
Path 1 Path 2 Path 3
Checkpoint
strategy 15 -5 -1 1 -2 2
Checkpoint
strategy 2-5 5 1 -1 -2 2
Attacker oracle
Path 1 Path 2 Path 3
Checkpoint
strategy 15 -5 -1 1 -2 2
Checkpoint
strategy 2-5 5 1 -1 -2 2
59
Double Oracle[2013]Incremental Strategy Generation Exploit Small Support
22
150 edges 2 Checkpoints
Only six candidate edges
for checkpoints
Jain
5923
Mumbai Police Checkpoints[2013]Results of Scale-up
20416 Roads15 checkpoints
20 min
59
Double Oracle Social Cyber Networks[2013]
Incremental Strategy Generation
24
Social networkseg counter-insurgency
Sources
Intermediate
Nodes
Links
Targets
Cyber networks
Tsai
59
Outline ldquoSecurity Gamesrdquo Research
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
25
59
Port Security Threat Scenarios
US Ports $315 trillion economy
USS Cole after suicide attack
French oil tanker hit by small boat
Attack on a ferry
26
59
PROTECT Randomized Patrol Scheduling [2013]
27
Port Protection (Scale-up) and Ferries (Continuous Spacetime)
59
PROTECT Randomized Patrol Scheduling [2013]Port Protection (Scale-up) and Ferries (Continuous Spacetime)
28
59
Ferries Scale-up with Mobile Resources amp Moving TargetsTransition Graph Representation
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
29
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Ferries Scale-up with Mobile Resources amp Moving TargetsTransition Graph Representation
30
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
31
Patrols protect nearby ferry location Solve as done in ARMOR
Ferries Patrol RoutesExponential Numbers of Patrol Routes
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Patrols protect nearby ferry location Solve as done in ARMOR
Ferries Patrol RoutesExponential Numbers of Patrol Routes
32
Pr([(B5) (C 10) (C15)]) = 017
Pr([(B5) (C10) (B15)]) =013
NT variables
Pr([(A5) (A10) (B15)]) = 007
Pr([(A5) (A10) (A15)]) = 003
Jiang KiekintveldFang
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Variables NOT routes but probability flow over each segment
Ferries Scale-up Marginal Probabilities Over Segments
33
NT variables
Jiang KiekintveldFang
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
030
017
013
Obtain marginal probabilities over segments
010
007
003
N2T variablesExtract Pr([(B5) (C 10) (C15)]) = 017
Pr([(B5) (C10) (B15)]) =013
Ferries Scale-up with Marginals Over Separable SegmentsSignificant Speedup
34
NT variables
59
5936
Outline ldquoSecurity Gamesrdquo Research (2007-Now)
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
Evaluation II Real-world deployments
(Patience)
Evaluation I Scale up Handle uncertainty
59
TRUSTS Frequent adversary interaction games
Patrols Against Fare Evaders
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
017
013
010
010
37
59
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
38
Jiang Delle Fave
59
Markov Decision Problems in Security games
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
39
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution Jiang Delle Fave
5940
Urban Transportation Security
MDPs amp DEC-MDPs in Security Games
COPS LA Metro System(Against Opportunistic Crime)
STREETS Singapore Roads(Against Reckless Driving)
DEC-MDPs in Security Games Teamwork + Uncertainty
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
ZhangShieh
59
Uncertainty Space Algorithms
Bayesian and Robust Approaches
Adversary payoff uncertainty
Adversary observation amp
defender execution
uncertainty
Adversary rationality
uncertainty
RECON
BRASS
Monotonic Maximin
(Monotonic adversary)
41
URAC
Payoff interval
Not point
estimateGMC HUNTER
Bayesian
Robust
Yin Nguyen An
0
05
1
15
5 10
Def
end
ers
EU
Targets
ISG RECON URAC
59
Security Games Environmental Crime
amp Bounded Rationality
FisheryGulf of Mexico
Nakai Nam Theun
Forest Area Laos
WildlifeQueen Elizabeth National
ParkUganda
No patrols
Higher
density
Lower densityx
Yang Ford Brown
59
Uncertainty in Adversary Decision Bounded Rationality
Human Subjects as Poachers
59
Uncertainty in Adversary Decision[2009]
Human subjects Anchoring e-Optimality
-4
-3
-2
-1
0
1
Unobserved 5 Observations 20 Observations Unlimited
Average expected reward
DOBSS
Uniform
COBRA
ARMOR
ARMOR Outperforms uniform random
COBRA
MqxCaq
Xxxts
qxR
ji
Xi
ijj
Xi Qj
jiijqx
)1()()1(
|)|1()1(
max
ee
Anchoring
e-optimality
With Sarit Kraus 44
Pita
5945
Quantal Response Stochastic choice better choice more likely
Quantal Response Model of Adversary [2011] Not Maximize Expected Utility
T
j
jxadversary
EU
jxadversary
EU
j
e
eq
1
))((
))((
-3
-25
-2
-15
-1
-05
0
05
1
15
2
Payoff 1 Payoff 2 Payoff 3 Payoff 4
QuantalResponse
COBRA
ARMOR
Adversaryrsquos probability
of choosing target j
Yang
10
)(max1
1
)(
)(
t tt
defendT
jT
j
jxEU
jxEU
x
xKxts
jxEU
e
e
adversary
adversary
59
Uncertainty in Adversary Decision [2012]
Robust vs Modeling Adversaries
Robustness Bound loss to defender Not model attacker via QR
Defeating Robust Learned subjective utility
Robust wins Draw QR wins
42 52 6
Results on 100 games
SU-QR wins Draw Robust
13 8 1
Results on 22 games
MqxCa
qxR
ji
Xi
ij
jiij
Xi Qj
qx
)1()(0
max
β (Adversaryrsquos utility loss if
deviates from optimal) gt=
(Defenderrsquos utility loss due to
adversary deviation)
penaltyattackw
rewardattackw
probcapturewjaSEU
3
2
1)(
M
j
jxSEU
jxSEU
jadversary
adversary
e
eq
1
)(
)(
SU-QR wins Draw Robust
6 13 3
Results against security experts
With Sarit Kraus 46
5947
PAWS Protection Assistant for Wildlife Security[2014]Repeated Stackelberg Game
Simulation
Learn from crime data
Improve model
Defender calculates
mixed strategy
Defender executes
randomized patrols
Poachers attack targets
119890119878119864119880119894(w1w2w3)
119896 119890119878119864119880119896(w1w2w3)
Bayesian SUQR Heterogeneous Poachers
5948
PAWS Test April 2014
Trials in Queen Elizabeth National Park
Andrew Lemieux with rangers
on PAWS patrol in Uganda
59
Security Resource OptimizationEvaluating Deployed Security Systems Not Easy
Game theory Improvement over previous approaches
Previous Human schedulers or ldquosimple randomrdquo
49
Lab
Evaluation
Simulated
adversary
Human subject
adversaries
Field Evaluation
Patrol quality
Unpredictable Cover
Compare real schedules
Scheduling competition
Expert evaluation
Field Evaluation
Tests against adversaries
ldquoMock attackersrdquo
Capture rates of
real adversaries
59
Why Does Game Theory Perform Better
Weaknesses of Previous Methods
Human schedulers
Predictable patterns eg US Coast Guard
Scheduling effort amp cognitive burden
Simple random (eg dice roll)
Wrong weightscoverage eg officers to sparsely crowded terminals
No adversary reactions
Multiple deployments over multiple years without us forcing them
50
59
Lab Evaluation via Simulations
Example from IRIS (FAMS)
-10
-8
-6
-4
-2
0
2
4
6
50 150 250
Defe
nde
r E
xpe
cte
d u
tilit
y
Schedule Size
Uniform Weighted random 1 Weighted random 2 IRIS
51
59
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage
Patrols Before PROTECT Boston Patrols After PROTECT Boston
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Base Patrol Area
52
PROTECT (Coast Guard) 350 increase defender expected utility
59
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage for Less Effort
53
IRIS for FAMS Outperformed expert human over six monthsReportGAO-09-903T
ARMOR at LAX Savings of up to an hour a day in scheduling
59
Field Evaluation Human vs Game Theory Competition
Counter-terrorism Patrol Scheduling
90 officers on LA Metro Trains
Humans required significant effort
Worse schedules than game theory
Observerrsquos report on questions
54
3
35
4
45
5
55
Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Q9
Q10
Q11
Q12
Secu
rity
Sco
re
Human Game Theory
59
Field Test Against Adversaries Mock Attackers
Example from PROTECT
ldquoMock attackerrdquo team deployed in Boston
Comparing PRE- to POST-PROTECT ldquodeterrencerdquo improved
Additional real-world indicators from Boston
Boston boaters questions
ldquohas the Coast Guard recently acquired more boatsrdquo
POST-PROTECT Actual reports of illegal activity
55
5956
Field Tests Against Adversaries
Computational Game Theory in the Field
Game theory vs Random
21 days of patrol
Identical conditions
Random + Human
Not controlled
0
20
40
60
80
100 Miscellaneous
Drugs
Firearm Violations
0
5
10
15
20
Captures30 min
Warnings30 min
Violations30 min
Game Theory
Rand+Human
Controlled
59
Expert Evaluation
Example from ARMOR IRIS amp PROTECT
February 2009 Commendations
LAX Police (City of Los Angeles)
July 2011 Operational Excellence
Award (US Coast Guard Boston)
September 2011 Certificate of
Appreciation (Federal Air Marshals)
June 2013 Meritorious Team Commendation
from Commandant (US Coast Guard)
57
59
Summary Security Games
Decision aids based on computational game theory in daily use
Optimize limited security resources against adversaries
Applications yield research challenges Science of security games
Scale-up Incremental strategy generation amp Marginals
Uncertainty Integrate MDPs Robustness Quantal response
Current applications (wildlife security) Interdisciplinar challenge
Global challenges Merge planninglearning amp security games
58
59
Just the Beginning of ldquoSecurity Gamesrdquohellip
Our next steps
59
Thank you
to sponsors
StartupARMORWAY Game theory in the field
Follow on research amp applications
Privacy audits
(Sinha IJCAIrsquo13)
Software testing
(Kukreja ASErsquo13)
Sport event security
(Yin AAAIrsquo14)
Singapore train
(Varakantham IAAIrsquo13)
Exam questions
(Li IJCAIrsquo13)
59
THANK YOU
tambeuscedu
httpteamcoreuscedusecurity
60
59
Example Model
Stackelberg Security Games
Security allocation
Targets have weights
Adversary surveillance
Target
1
Target
2
Target 1 4 -3 -1 1
Target 2 -5 5 2 -1
Adversary
5
Defender
59
Stackelberg Security GamesSecurity Resource Optimization Not 100 Security
Random strategy
Increase costuncertainty to attackers
Stackelberg game
Defender commits to mixed strategy
Adversary conducts surveillance responds
Stackelberg Equilibrium Optimal random
Target
1
Target
2
Target 1 4 -3 -1 1
Target 2 -5 5 2 -1
Adversary
6
Defender
59
Research Contributions
Game Theory for Security
Computational Game Theory in the Field
Computational game
theory
bull Massive games
Behavioral game
theory
bull Exploit human
behavior
models
+ Planning under uncertainty learninghellip
7
859
Ports amp port traffic
US Coast Guard
Applications Deployed Security Assistants
Airports access
roads amp flights
TSA
Airport Police
Urban transport
LA SheriffrsquosTSA
Singapore Police
Environment
US Coast Guard
WWF WCShellip
59
Key Lessons Security Games
Decision aids based on computational game theory in daily use
Optimize limited security resources against adversaries
Applications yield research challenges Science of security games
Scale-up Incremental strategy generation amp Marginals
Uncertainty Integrate MDPs Robustness Quantal response
Current applications (wildlife security) Interdisciplinary challenge
Global challenges Merge planninglearning amp security games
9
5910
Outline ldquoSecurity Gamesrdquo Research (2007-Now)
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
Evaluation II Real-world deployments
(Patience)
Evaluation I Scale up Handle uncertainty
Publications
AAMAS AAAI IJCAIhellip
2007 onwards
5911
bull 6 plots against LAX
ARMOR LAX (2007) GUARDS TSA (2011)
Airport Security Mapping to Stackelberg Games
GLASGOW 63007
5912
ARMOR Operation [2007]Generate Detailed Defender Schedule Pita Paruchuri
Mixed Integer Program
Pr(Canine patrol 8 AM Terminals 357) = 033
Pr(Canine patrol 8 AM Terminals 256) = 017
helliphellipCanine Team Schedule July 28Term 1 Term 2 Term 3 Term 4 Term 5 Term 6 Term 7 Term 8
8 AM Team1 Team3 Team5
9 AM Team1 Team2 Team4
10 AM Team3 Team5 Team2
Target 1 Target 2
Defender 1 2 -1 -3 4
Defender 2 -3 3 3 -2
5913
ARMOR MIP [2007]Generate Mixed Strategy for Defender
1 i
ixts
jq
ix
ijR
Xi Qj
max
1Qj
jq
MqxCa ji
Xi
ij )1()(0
Maximize defender
expected utility
Defender mixed
strategy
Adversary best
response
Pita Paruchuri
Target 1 Target 2
Defender 1 2 -1 -3 4
Defender 2 -3 3 3 -2
Adversary response
5914
ARMOR Payoffs [2007]Previous Research Provides Payoffs in Security Game Domains
Target 1 Target 2
Defender 1 2 -1 -3 4
Defender 2 -3 3 3 -2
jq
ix
ijR
Xi Qj
maxMaximize defender
expected utility
5915
ARMOR MIP [2007]Solving for a Single Adversary Type
Term 1 Term 2
Defend1 2 -1 -3 4
Defend2 -3 1 3 -3
1 i
ixts
jq
ix
ijR
Xi Qj
max
1Qj
jq
ljq][ix
MqxCa ji
Xi
ij
1010
)1()(0
Maximize defender
expected utility
Defender strategy
Adversary strategy
Adversary best
response
119909119894
ARMORhellipthrows a digital cloak of invisibilityhellip
59
IRIS Federal Air Marshals Service [2009]Scale Up Number of Defender Strategies
1000 Flights 20 air marshals 1041
combinations
ARMOR out of memory
Strategy 1 Strategy 2 Strategy 3
Strateg
y 1
Strateg
y 2
Strateg
y 3
Strateg
y 4
Strateg
y 5
Strateg
y 6
Not enumerate all combinations
Branch and price Incremental strategy generation
Strategy 1 Strategy 2 Strategy 3
Strategy 1
Strategy 2
Strategy 3
Strategy 4
Strategy 5
Strategy 6
16
59
IRIS Scale Up Number of Defender Strategies [2009]
Small Support Set for Mixed Strategies
Small support set size
bull Most xi variables zero
x124=0239
x123=00
x135=00
Attack
1
Attack
2
Attack
hellip
Attack
1000
123 5-10 4-8 hellip -209
124 5-10 4-8 hellip -209
135 5-10 -95 hellip -209
hellip
hellip1041 rowsx378=0123
10]10[
)1()(0
11
max
jqx
MqxCa
qxts
qxR
i
ji
Xi
ij
Qj
j
i
i
jiij
Xi Qj
qx
1000 flights 20 air marshals
1041combinations
17
59
Target 3 Target 7
hellip hellip
Resource Sink
Best new pure strategy
Minimum cost network flow
IRIS Incremental Strategy Generation
Exploit Small Support
Attack 1 Attack 2 Attack Attack 6
124 5-10 4-8 hellip -209
378 -8 10 -810 hellip -810
hellip
500 rows
NOT 1041
Attack 1 Attack 2 Attackhellip Attack 6
124 5-10 4-8 hellip -209
Slave (LP Duality Theory)
Master
Converge
GLOBAL
OPTIMAL
Attack 1 Attack 2 Attackhellip Attack 6
124 5-10 4-8 hellip -209
378 -8 10 -810 hellip -810
18
Jain Kiekintveld
59
IRIS Deployed FAMS (2009-)
ldquohellipin 2011 the Military Operations Research Society selected a University
of Southern California project with FAMS on randomizing flight
schedules for the prestigious Rist Awardhelliprdquo
-R S Bray (TSA)
Transportation Security Subcommittee
US House of Representatives 2012
19
Significant change in operations
59
Networks Mumbai Police Checkpoints[2013]
150 edges 2 Checkpoints
150-choose-2 strategies
With V Conitzer 20
Jain
59
Networks Mumbai Police Checkpoints[2013]
Incremental Strategy Generation
With V Conitzer 21
Jain
Double oracle Converge to a global optimal
Path 1 Path 2
Checkpoint
strategy 15 -5 -1 1
Path 1 Path 2
Checkpoint
strategy 15 -5 -1 1
Checkpoint
strategy 2-5 5 2 -1
Defender oracle
Path 1 Path 2 Path 3
Checkpoint
strategy 15 -5 -1 1 -2 2
Checkpoint
strategy 2-5 5 1 -1 -2 2
Attacker oracle
Path 1 Path 2 Path 3
Checkpoint
strategy 15 -5 -1 1 -2 2
Checkpoint
strategy 2-5 5 1 -1 -2 2
59
Double Oracle[2013]Incremental Strategy Generation Exploit Small Support
22
150 edges 2 Checkpoints
Only six candidate edges
for checkpoints
Jain
5923
Mumbai Police Checkpoints[2013]Results of Scale-up
20416 Roads15 checkpoints
20 min
59
Double Oracle Social Cyber Networks[2013]
Incremental Strategy Generation
24
Social networkseg counter-insurgency
Sources
Intermediate
Nodes
Links
Targets
Cyber networks
Tsai
59
Outline ldquoSecurity Gamesrdquo Research
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
25
59
Port Security Threat Scenarios
US Ports $315 trillion economy
USS Cole after suicide attack
French oil tanker hit by small boat
Attack on a ferry
26
59
PROTECT Randomized Patrol Scheduling [2013]
27
Port Protection (Scale-up) and Ferries (Continuous Spacetime)
59
PROTECT Randomized Patrol Scheduling [2013]Port Protection (Scale-up) and Ferries (Continuous Spacetime)
28
59
Ferries Scale-up with Mobile Resources amp Moving TargetsTransition Graph Representation
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
29
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Ferries Scale-up with Mobile Resources amp Moving TargetsTransition Graph Representation
30
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
31
Patrols protect nearby ferry location Solve as done in ARMOR
Ferries Patrol RoutesExponential Numbers of Patrol Routes
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Patrols protect nearby ferry location Solve as done in ARMOR
Ferries Patrol RoutesExponential Numbers of Patrol Routes
32
Pr([(B5) (C 10) (C15)]) = 017
Pr([(B5) (C10) (B15)]) =013
NT variables
Pr([(A5) (A10) (B15)]) = 007
Pr([(A5) (A10) (A15)]) = 003
Jiang KiekintveldFang
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Variables NOT routes but probability flow over each segment
Ferries Scale-up Marginal Probabilities Over Segments
33
NT variables
Jiang KiekintveldFang
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
030
017
013
Obtain marginal probabilities over segments
010
007
003
N2T variablesExtract Pr([(B5) (C 10) (C15)]) = 017
Pr([(B5) (C10) (B15)]) =013
Ferries Scale-up with Marginals Over Separable SegmentsSignificant Speedup
34
NT variables
59
5936
Outline ldquoSecurity Gamesrdquo Research (2007-Now)
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
Evaluation II Real-world deployments
(Patience)
Evaluation I Scale up Handle uncertainty
59
TRUSTS Frequent adversary interaction games
Patrols Against Fare Evaders
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
017
013
010
010
37
59
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
38
Jiang Delle Fave
59
Markov Decision Problems in Security games
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
39
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution Jiang Delle Fave
5940
Urban Transportation Security
MDPs amp DEC-MDPs in Security Games
COPS LA Metro System(Against Opportunistic Crime)
STREETS Singapore Roads(Against Reckless Driving)
DEC-MDPs in Security Games Teamwork + Uncertainty
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
ZhangShieh
59
Uncertainty Space Algorithms
Bayesian and Robust Approaches
Adversary payoff uncertainty
Adversary observation amp
defender execution
uncertainty
Adversary rationality
uncertainty
RECON
BRASS
Monotonic Maximin
(Monotonic adversary)
41
URAC
Payoff interval
Not point
estimateGMC HUNTER
Bayesian
Robust
Yin Nguyen An
0
05
1
15
5 10
Def
end
ers
EU
Targets
ISG RECON URAC
59
Security Games Environmental Crime
amp Bounded Rationality
FisheryGulf of Mexico
Nakai Nam Theun
Forest Area Laos
WildlifeQueen Elizabeth National
ParkUganda
No patrols
Higher
density
Lower densityx
Yang Ford Brown
59
Uncertainty in Adversary Decision Bounded Rationality
Human Subjects as Poachers
59
Uncertainty in Adversary Decision[2009]
Human subjects Anchoring e-Optimality
-4
-3
-2
-1
0
1
Unobserved 5 Observations 20 Observations Unlimited
Average expected reward
DOBSS
Uniform
COBRA
ARMOR
ARMOR Outperforms uniform random
COBRA
MqxCaq
Xxxts
qxR
ji
Xi
ijj
Xi Qj
jiijqx
)1()()1(
|)|1()1(
max
ee
Anchoring
e-optimality
With Sarit Kraus 44
Pita
5945
Quantal Response Stochastic choice better choice more likely
Quantal Response Model of Adversary [2011] Not Maximize Expected Utility
T
j
jxadversary
EU
jxadversary
EU
j
e
eq
1
))((
))((
-3
-25
-2
-15
-1
-05
0
05
1
15
2
Payoff 1 Payoff 2 Payoff 3 Payoff 4
QuantalResponse
COBRA
ARMOR
Adversaryrsquos probability
of choosing target j
Yang
10
)(max1
1
)(
)(
t tt
defendT
jT
j
jxEU
jxEU
x
xKxts
jxEU
e
e
adversary
adversary
59
Uncertainty in Adversary Decision [2012]
Robust vs Modeling Adversaries
Robustness Bound loss to defender Not model attacker via QR
Defeating Robust Learned subjective utility
Robust wins Draw QR wins
42 52 6
Results on 100 games
SU-QR wins Draw Robust
13 8 1
Results on 22 games
MqxCa
qxR
ji
Xi
ij
jiij
Xi Qj
qx
)1()(0
max
β (Adversaryrsquos utility loss if
deviates from optimal) gt=
(Defenderrsquos utility loss due to
adversary deviation)
penaltyattackw
rewardattackw
probcapturewjaSEU
3
2
1)(
M
j
jxSEU
jxSEU
jadversary
adversary
e
eq
1
)(
)(
SU-QR wins Draw Robust
6 13 3
Results against security experts
With Sarit Kraus 46
5947
PAWS Protection Assistant for Wildlife Security[2014]Repeated Stackelberg Game
Simulation
Learn from crime data
Improve model
Defender calculates
mixed strategy
Defender executes
randomized patrols
Poachers attack targets
119890119878119864119880119894(w1w2w3)
119896 119890119878119864119880119896(w1w2w3)
Bayesian SUQR Heterogeneous Poachers
5948
PAWS Test April 2014
Trials in Queen Elizabeth National Park
Andrew Lemieux with rangers
on PAWS patrol in Uganda
59
Security Resource OptimizationEvaluating Deployed Security Systems Not Easy
Game theory Improvement over previous approaches
Previous Human schedulers or ldquosimple randomrdquo
49
Lab
Evaluation
Simulated
adversary
Human subject
adversaries
Field Evaluation
Patrol quality
Unpredictable Cover
Compare real schedules
Scheduling competition
Expert evaluation
Field Evaluation
Tests against adversaries
ldquoMock attackersrdquo
Capture rates of
real adversaries
59
Why Does Game Theory Perform Better
Weaknesses of Previous Methods
Human schedulers
Predictable patterns eg US Coast Guard
Scheduling effort amp cognitive burden
Simple random (eg dice roll)
Wrong weightscoverage eg officers to sparsely crowded terminals
No adversary reactions
Multiple deployments over multiple years without us forcing them
50
59
Lab Evaluation via Simulations
Example from IRIS (FAMS)
-10
-8
-6
-4
-2
0
2
4
6
50 150 250
Defe
nde
r E
xpe
cte
d u
tilit
y
Schedule Size
Uniform Weighted random 1 Weighted random 2 IRIS
51
59
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage
Patrols Before PROTECT Boston Patrols After PROTECT Boston
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Base Patrol Area
52
PROTECT (Coast Guard) 350 increase defender expected utility
59
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage for Less Effort
53
IRIS for FAMS Outperformed expert human over six monthsReportGAO-09-903T
ARMOR at LAX Savings of up to an hour a day in scheduling
59
Field Evaluation Human vs Game Theory Competition
Counter-terrorism Patrol Scheduling
90 officers on LA Metro Trains
Humans required significant effort
Worse schedules than game theory
Observerrsquos report on questions
54
3
35
4
45
5
55
Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Q9
Q10
Q11
Q12
Secu
rity
Sco
re
Human Game Theory
59
Field Test Against Adversaries Mock Attackers
Example from PROTECT
ldquoMock attackerrdquo team deployed in Boston
Comparing PRE- to POST-PROTECT ldquodeterrencerdquo improved
Additional real-world indicators from Boston
Boston boaters questions
ldquohas the Coast Guard recently acquired more boatsrdquo
POST-PROTECT Actual reports of illegal activity
55
5956
Field Tests Against Adversaries
Computational Game Theory in the Field
Game theory vs Random
21 days of patrol
Identical conditions
Random + Human
Not controlled
0
20
40
60
80
100 Miscellaneous
Drugs
Firearm Violations
0
5
10
15
20
Captures30 min
Warnings30 min
Violations30 min
Game Theory
Rand+Human
Controlled
59
Expert Evaluation
Example from ARMOR IRIS amp PROTECT
February 2009 Commendations
LAX Police (City of Los Angeles)
July 2011 Operational Excellence
Award (US Coast Guard Boston)
September 2011 Certificate of
Appreciation (Federal Air Marshals)
June 2013 Meritorious Team Commendation
from Commandant (US Coast Guard)
57
59
Summary Security Games
Decision aids based on computational game theory in daily use
Optimize limited security resources against adversaries
Applications yield research challenges Science of security games
Scale-up Incremental strategy generation amp Marginals
Uncertainty Integrate MDPs Robustness Quantal response
Current applications (wildlife security) Interdisciplinar challenge
Global challenges Merge planninglearning amp security games
58
59
Just the Beginning of ldquoSecurity Gamesrdquohellip
Our next steps
59
Thank you
to sponsors
StartupARMORWAY Game theory in the field
Follow on research amp applications
Privacy audits
(Sinha IJCAIrsquo13)
Software testing
(Kukreja ASErsquo13)
Sport event security
(Yin AAAIrsquo14)
Singapore train
(Varakantham IAAIrsquo13)
Exam questions
(Li IJCAIrsquo13)
59
THANK YOU
tambeuscedu
httpteamcoreuscedusecurity
60
59
Stackelberg Security GamesSecurity Resource Optimization Not 100 Security
Random strategy
Increase costuncertainty to attackers
Stackelberg game
Defender commits to mixed strategy
Adversary conducts surveillance responds
Stackelberg Equilibrium Optimal random
Target
1
Target
2
Target 1 4 -3 -1 1
Target 2 -5 5 2 -1
Adversary
6
Defender
59
Research Contributions
Game Theory for Security
Computational Game Theory in the Field
Computational game
theory
bull Massive games
Behavioral game
theory
bull Exploit human
behavior
models
+ Planning under uncertainty learninghellip
7
859
Ports amp port traffic
US Coast Guard
Applications Deployed Security Assistants
Airports access
roads amp flights
TSA
Airport Police
Urban transport
LA SheriffrsquosTSA
Singapore Police
Environment
US Coast Guard
WWF WCShellip
59
Key Lessons Security Games
Decision aids based on computational game theory in daily use
Optimize limited security resources against adversaries
Applications yield research challenges Science of security games
Scale-up Incremental strategy generation amp Marginals
Uncertainty Integrate MDPs Robustness Quantal response
Current applications (wildlife security) Interdisciplinary challenge
Global challenges Merge planninglearning amp security games
9
5910
Outline ldquoSecurity Gamesrdquo Research (2007-Now)
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
Evaluation II Real-world deployments
(Patience)
Evaluation I Scale up Handle uncertainty
Publications
AAMAS AAAI IJCAIhellip
2007 onwards
5911
bull 6 plots against LAX
ARMOR LAX (2007) GUARDS TSA (2011)
Airport Security Mapping to Stackelberg Games
GLASGOW 63007
5912
ARMOR Operation [2007]Generate Detailed Defender Schedule Pita Paruchuri
Mixed Integer Program
Pr(Canine patrol 8 AM Terminals 357) = 033
Pr(Canine patrol 8 AM Terminals 256) = 017
helliphellipCanine Team Schedule July 28Term 1 Term 2 Term 3 Term 4 Term 5 Term 6 Term 7 Term 8
8 AM Team1 Team3 Team5
9 AM Team1 Team2 Team4
10 AM Team3 Team5 Team2
Target 1 Target 2
Defender 1 2 -1 -3 4
Defender 2 -3 3 3 -2
5913
ARMOR MIP [2007]Generate Mixed Strategy for Defender
1 i
ixts
jq
ix
ijR
Xi Qj
max
1Qj
jq
MqxCa ji
Xi
ij )1()(0
Maximize defender
expected utility
Defender mixed
strategy
Adversary best
response
Pita Paruchuri
Target 1 Target 2
Defender 1 2 -1 -3 4
Defender 2 -3 3 3 -2
Adversary response
5914
ARMOR Payoffs [2007]Previous Research Provides Payoffs in Security Game Domains
Target 1 Target 2
Defender 1 2 -1 -3 4
Defender 2 -3 3 3 -2
jq
ix
ijR
Xi Qj
maxMaximize defender
expected utility
5915
ARMOR MIP [2007]Solving for a Single Adversary Type
Term 1 Term 2
Defend1 2 -1 -3 4
Defend2 -3 1 3 -3
1 i
ixts
jq
ix
ijR
Xi Qj
max
1Qj
jq
ljq][ix
MqxCa ji
Xi
ij
1010
)1()(0
Maximize defender
expected utility
Defender strategy
Adversary strategy
Adversary best
response
119909119894
ARMORhellipthrows a digital cloak of invisibilityhellip
59
IRIS Federal Air Marshals Service [2009]Scale Up Number of Defender Strategies
1000 Flights 20 air marshals 1041
combinations
ARMOR out of memory
Strategy 1 Strategy 2 Strategy 3
Strateg
y 1
Strateg
y 2
Strateg
y 3
Strateg
y 4
Strateg
y 5
Strateg
y 6
Not enumerate all combinations
Branch and price Incremental strategy generation
Strategy 1 Strategy 2 Strategy 3
Strategy 1
Strategy 2
Strategy 3
Strategy 4
Strategy 5
Strategy 6
16
59
IRIS Scale Up Number of Defender Strategies [2009]
Small Support Set for Mixed Strategies
Small support set size
bull Most xi variables zero
x124=0239
x123=00
x135=00
Attack
1
Attack
2
Attack
hellip
Attack
1000
123 5-10 4-8 hellip -209
124 5-10 4-8 hellip -209
135 5-10 -95 hellip -209
hellip
hellip1041 rowsx378=0123
10]10[
)1()(0
11
max
jqx
MqxCa
qxts
qxR
i
ji
Xi
ij
Qj
j
i
i
jiij
Xi Qj
qx
1000 flights 20 air marshals
1041combinations
17
59
Target 3 Target 7
hellip hellip
Resource Sink
Best new pure strategy
Minimum cost network flow
IRIS Incremental Strategy Generation
Exploit Small Support
Attack 1 Attack 2 Attack Attack 6
124 5-10 4-8 hellip -209
378 -8 10 -810 hellip -810
hellip
500 rows
NOT 1041
Attack 1 Attack 2 Attackhellip Attack 6
124 5-10 4-8 hellip -209
Slave (LP Duality Theory)
Master
Converge
GLOBAL
OPTIMAL
Attack 1 Attack 2 Attackhellip Attack 6
124 5-10 4-8 hellip -209
378 -8 10 -810 hellip -810
18
Jain Kiekintveld
59
IRIS Deployed FAMS (2009-)
ldquohellipin 2011 the Military Operations Research Society selected a University
of Southern California project with FAMS on randomizing flight
schedules for the prestigious Rist Awardhelliprdquo
-R S Bray (TSA)
Transportation Security Subcommittee
US House of Representatives 2012
19
Significant change in operations
59
Networks Mumbai Police Checkpoints[2013]
150 edges 2 Checkpoints
150-choose-2 strategies
With V Conitzer 20
Jain
59
Networks Mumbai Police Checkpoints[2013]
Incremental Strategy Generation
With V Conitzer 21
Jain
Double oracle Converge to a global optimal
Path 1 Path 2
Checkpoint
strategy 15 -5 -1 1
Path 1 Path 2
Checkpoint
strategy 15 -5 -1 1
Checkpoint
strategy 2-5 5 2 -1
Defender oracle
Path 1 Path 2 Path 3
Checkpoint
strategy 15 -5 -1 1 -2 2
Checkpoint
strategy 2-5 5 1 -1 -2 2
Attacker oracle
Path 1 Path 2 Path 3
Checkpoint
strategy 15 -5 -1 1 -2 2
Checkpoint
strategy 2-5 5 1 -1 -2 2
59
Double Oracle[2013]Incremental Strategy Generation Exploit Small Support
22
150 edges 2 Checkpoints
Only six candidate edges
for checkpoints
Jain
5923
Mumbai Police Checkpoints[2013]Results of Scale-up
20416 Roads15 checkpoints
20 min
59
Double Oracle Social Cyber Networks[2013]
Incremental Strategy Generation
24
Social networkseg counter-insurgency
Sources
Intermediate
Nodes
Links
Targets
Cyber networks
Tsai
59
Outline ldquoSecurity Gamesrdquo Research
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
25
59
Port Security Threat Scenarios
US Ports $315 trillion economy
USS Cole after suicide attack
French oil tanker hit by small boat
Attack on a ferry
26
59
PROTECT Randomized Patrol Scheduling [2013]
27
Port Protection (Scale-up) and Ferries (Continuous Spacetime)
59
PROTECT Randomized Patrol Scheduling [2013]Port Protection (Scale-up) and Ferries (Continuous Spacetime)
28
59
Ferries Scale-up with Mobile Resources amp Moving TargetsTransition Graph Representation
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
29
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Ferries Scale-up with Mobile Resources amp Moving TargetsTransition Graph Representation
30
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
31
Patrols protect nearby ferry location Solve as done in ARMOR
Ferries Patrol RoutesExponential Numbers of Patrol Routes
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Patrols protect nearby ferry location Solve as done in ARMOR
Ferries Patrol RoutesExponential Numbers of Patrol Routes
32
Pr([(B5) (C 10) (C15)]) = 017
Pr([(B5) (C10) (B15)]) =013
NT variables
Pr([(A5) (A10) (B15)]) = 007
Pr([(A5) (A10) (A15)]) = 003
Jiang KiekintveldFang
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Variables NOT routes but probability flow over each segment
Ferries Scale-up Marginal Probabilities Over Segments
33
NT variables
Jiang KiekintveldFang
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
030
017
013
Obtain marginal probabilities over segments
010
007
003
N2T variablesExtract Pr([(B5) (C 10) (C15)]) = 017
Pr([(B5) (C10) (B15)]) =013
Ferries Scale-up with Marginals Over Separable SegmentsSignificant Speedup
34
NT variables
59
5936
Outline ldquoSecurity Gamesrdquo Research (2007-Now)
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
Evaluation II Real-world deployments
(Patience)
Evaluation I Scale up Handle uncertainty
59
TRUSTS Frequent adversary interaction games
Patrols Against Fare Evaders
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
017
013
010
010
37
59
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
38
Jiang Delle Fave
59
Markov Decision Problems in Security games
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
39
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution Jiang Delle Fave
5940
Urban Transportation Security
MDPs amp DEC-MDPs in Security Games
COPS LA Metro System(Against Opportunistic Crime)
STREETS Singapore Roads(Against Reckless Driving)
DEC-MDPs in Security Games Teamwork + Uncertainty
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
ZhangShieh
59
Uncertainty Space Algorithms
Bayesian and Robust Approaches
Adversary payoff uncertainty
Adversary observation amp
defender execution
uncertainty
Adversary rationality
uncertainty
RECON
BRASS
Monotonic Maximin
(Monotonic adversary)
41
URAC
Payoff interval
Not point
estimateGMC HUNTER
Bayesian
Robust
Yin Nguyen An
0
05
1
15
5 10
Def
end
ers
EU
Targets
ISG RECON URAC
59
Security Games Environmental Crime
amp Bounded Rationality
FisheryGulf of Mexico
Nakai Nam Theun
Forest Area Laos
WildlifeQueen Elizabeth National
ParkUganda
No patrols
Higher
density
Lower densityx
Yang Ford Brown
59
Uncertainty in Adversary Decision Bounded Rationality
Human Subjects as Poachers
59
Uncertainty in Adversary Decision[2009]
Human subjects Anchoring e-Optimality
-4
-3
-2
-1
0
1
Unobserved 5 Observations 20 Observations Unlimited
Average expected reward
DOBSS
Uniform
COBRA
ARMOR
ARMOR Outperforms uniform random
COBRA
MqxCaq
Xxxts
qxR
ji
Xi
ijj
Xi Qj
jiijqx
)1()()1(
|)|1()1(
max
ee
Anchoring
e-optimality
With Sarit Kraus 44
Pita
5945
Quantal Response Stochastic choice better choice more likely
Quantal Response Model of Adversary [2011] Not Maximize Expected Utility
T
j
jxadversary
EU
jxadversary
EU
j
e
eq
1
))((
))((
-3
-25
-2
-15
-1
-05
0
05
1
15
2
Payoff 1 Payoff 2 Payoff 3 Payoff 4
QuantalResponse
COBRA
ARMOR
Adversaryrsquos probability
of choosing target j
Yang
10
)(max1
1
)(
)(
t tt
defendT
jT
j
jxEU
jxEU
x
xKxts
jxEU
e
e
adversary
adversary
59
Uncertainty in Adversary Decision [2012]
Robust vs Modeling Adversaries
Robustness Bound loss to defender Not model attacker via QR
Defeating Robust Learned subjective utility
Robust wins Draw QR wins
42 52 6
Results on 100 games
SU-QR wins Draw Robust
13 8 1
Results on 22 games
MqxCa
qxR
ji
Xi
ij
jiij
Xi Qj
qx
)1()(0
max
β (Adversaryrsquos utility loss if
deviates from optimal) gt=
(Defenderrsquos utility loss due to
adversary deviation)
penaltyattackw
rewardattackw
probcapturewjaSEU
3
2
1)(
M
j
jxSEU
jxSEU
jadversary
adversary
e
eq
1
)(
)(
SU-QR wins Draw Robust
6 13 3
Results against security experts
With Sarit Kraus 46
5947
PAWS Protection Assistant for Wildlife Security[2014]Repeated Stackelberg Game
Simulation
Learn from crime data
Improve model
Defender calculates
mixed strategy
Defender executes
randomized patrols
Poachers attack targets
119890119878119864119880119894(w1w2w3)
119896 119890119878119864119880119896(w1w2w3)
Bayesian SUQR Heterogeneous Poachers
5948
PAWS Test April 2014
Trials in Queen Elizabeth National Park
Andrew Lemieux with rangers
on PAWS patrol in Uganda
59
Security Resource OptimizationEvaluating Deployed Security Systems Not Easy
Game theory Improvement over previous approaches
Previous Human schedulers or ldquosimple randomrdquo
49
Lab
Evaluation
Simulated
adversary
Human subject
adversaries
Field Evaluation
Patrol quality
Unpredictable Cover
Compare real schedules
Scheduling competition
Expert evaluation
Field Evaluation
Tests against adversaries
ldquoMock attackersrdquo
Capture rates of
real adversaries
59
Why Does Game Theory Perform Better
Weaknesses of Previous Methods
Human schedulers
Predictable patterns eg US Coast Guard
Scheduling effort amp cognitive burden
Simple random (eg dice roll)
Wrong weightscoverage eg officers to sparsely crowded terminals
No adversary reactions
Multiple deployments over multiple years without us forcing them
50
59
Lab Evaluation via Simulations
Example from IRIS (FAMS)
-10
-8
-6
-4
-2
0
2
4
6
50 150 250
Defe
nde
r E
xpe
cte
d u
tilit
y
Schedule Size
Uniform Weighted random 1 Weighted random 2 IRIS
51
59
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage
Patrols Before PROTECT Boston Patrols After PROTECT Boston
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Base Patrol Area
52
PROTECT (Coast Guard) 350 increase defender expected utility
59
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage for Less Effort
53
IRIS for FAMS Outperformed expert human over six monthsReportGAO-09-903T
ARMOR at LAX Savings of up to an hour a day in scheduling
59
Field Evaluation Human vs Game Theory Competition
Counter-terrorism Patrol Scheduling
90 officers on LA Metro Trains
Humans required significant effort
Worse schedules than game theory
Observerrsquos report on questions
54
3
35
4
45
5
55
Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Q9
Q10
Q11
Q12
Secu
rity
Sco
re
Human Game Theory
59
Field Test Against Adversaries Mock Attackers
Example from PROTECT
ldquoMock attackerrdquo team deployed in Boston
Comparing PRE- to POST-PROTECT ldquodeterrencerdquo improved
Additional real-world indicators from Boston
Boston boaters questions
ldquohas the Coast Guard recently acquired more boatsrdquo
POST-PROTECT Actual reports of illegal activity
55
5956
Field Tests Against Adversaries
Computational Game Theory in the Field
Game theory vs Random
21 days of patrol
Identical conditions
Random + Human
Not controlled
0
20
40
60
80
100 Miscellaneous
Drugs
Firearm Violations
0
5
10
15
20
Captures30 min
Warnings30 min
Violations30 min
Game Theory
Rand+Human
Controlled
59
Expert Evaluation
Example from ARMOR IRIS amp PROTECT
February 2009 Commendations
LAX Police (City of Los Angeles)
July 2011 Operational Excellence
Award (US Coast Guard Boston)
September 2011 Certificate of
Appreciation (Federal Air Marshals)
June 2013 Meritorious Team Commendation
from Commandant (US Coast Guard)
57
59
Summary Security Games
Decision aids based on computational game theory in daily use
Optimize limited security resources against adversaries
Applications yield research challenges Science of security games
Scale-up Incremental strategy generation amp Marginals
Uncertainty Integrate MDPs Robustness Quantal response
Current applications (wildlife security) Interdisciplinar challenge
Global challenges Merge planninglearning amp security games
58
59
Just the Beginning of ldquoSecurity Gamesrdquohellip
Our next steps
59
Thank you
to sponsors
StartupARMORWAY Game theory in the field
Follow on research amp applications
Privacy audits
(Sinha IJCAIrsquo13)
Software testing
(Kukreja ASErsquo13)
Sport event security
(Yin AAAIrsquo14)
Singapore train
(Varakantham IAAIrsquo13)
Exam questions
(Li IJCAIrsquo13)
59
THANK YOU
tambeuscedu
httpteamcoreuscedusecurity
60
59
Research Contributions
Game Theory for Security
Computational Game Theory in the Field
Computational game
theory
bull Massive games
Behavioral game
theory
bull Exploit human
behavior
models
+ Planning under uncertainty learninghellip
7
859
Ports amp port traffic
US Coast Guard
Applications Deployed Security Assistants
Airports access
roads amp flights
TSA
Airport Police
Urban transport
LA SheriffrsquosTSA
Singapore Police
Environment
US Coast Guard
WWF WCShellip
59
Key Lessons Security Games
Decision aids based on computational game theory in daily use
Optimize limited security resources against adversaries
Applications yield research challenges Science of security games
Scale-up Incremental strategy generation amp Marginals
Uncertainty Integrate MDPs Robustness Quantal response
Current applications (wildlife security) Interdisciplinary challenge
Global challenges Merge planninglearning amp security games
9
5910
Outline ldquoSecurity Gamesrdquo Research (2007-Now)
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
Evaluation II Real-world deployments
(Patience)
Evaluation I Scale up Handle uncertainty
Publications
AAMAS AAAI IJCAIhellip
2007 onwards
5911
bull 6 plots against LAX
ARMOR LAX (2007) GUARDS TSA (2011)
Airport Security Mapping to Stackelberg Games
GLASGOW 63007
5912
ARMOR Operation [2007]Generate Detailed Defender Schedule Pita Paruchuri
Mixed Integer Program
Pr(Canine patrol 8 AM Terminals 357) = 033
Pr(Canine patrol 8 AM Terminals 256) = 017
helliphellipCanine Team Schedule July 28Term 1 Term 2 Term 3 Term 4 Term 5 Term 6 Term 7 Term 8
8 AM Team1 Team3 Team5
9 AM Team1 Team2 Team4
10 AM Team3 Team5 Team2
Target 1 Target 2
Defender 1 2 -1 -3 4
Defender 2 -3 3 3 -2
5913
ARMOR MIP [2007]Generate Mixed Strategy for Defender
1 i
ixts
jq
ix
ijR
Xi Qj
max
1Qj
jq
MqxCa ji
Xi
ij )1()(0
Maximize defender
expected utility
Defender mixed
strategy
Adversary best
response
Pita Paruchuri
Target 1 Target 2
Defender 1 2 -1 -3 4
Defender 2 -3 3 3 -2
Adversary response
5914
ARMOR Payoffs [2007]Previous Research Provides Payoffs in Security Game Domains
Target 1 Target 2
Defender 1 2 -1 -3 4
Defender 2 -3 3 3 -2
jq
ix
ijR
Xi Qj
maxMaximize defender
expected utility
5915
ARMOR MIP [2007]Solving for a Single Adversary Type
Term 1 Term 2
Defend1 2 -1 -3 4
Defend2 -3 1 3 -3
1 i
ixts
jq
ix
ijR
Xi Qj
max
1Qj
jq
ljq][ix
MqxCa ji
Xi
ij
1010
)1()(0
Maximize defender
expected utility
Defender strategy
Adversary strategy
Adversary best
response
119909119894
ARMORhellipthrows a digital cloak of invisibilityhellip
59
IRIS Federal Air Marshals Service [2009]Scale Up Number of Defender Strategies
1000 Flights 20 air marshals 1041
combinations
ARMOR out of memory
Strategy 1 Strategy 2 Strategy 3
Strateg
y 1
Strateg
y 2
Strateg
y 3
Strateg
y 4
Strateg
y 5
Strateg
y 6
Not enumerate all combinations
Branch and price Incremental strategy generation
Strategy 1 Strategy 2 Strategy 3
Strategy 1
Strategy 2
Strategy 3
Strategy 4
Strategy 5
Strategy 6
16
59
IRIS Scale Up Number of Defender Strategies [2009]
Small Support Set for Mixed Strategies
Small support set size
bull Most xi variables zero
x124=0239
x123=00
x135=00
Attack
1
Attack
2
Attack
hellip
Attack
1000
123 5-10 4-8 hellip -209
124 5-10 4-8 hellip -209
135 5-10 -95 hellip -209
hellip
hellip1041 rowsx378=0123
10]10[
)1()(0
11
max
jqx
MqxCa
qxts
qxR
i
ji
Xi
ij
Qj
j
i
i
jiij
Xi Qj
qx
1000 flights 20 air marshals
1041combinations
17
59
Target 3 Target 7
hellip hellip
Resource Sink
Best new pure strategy
Minimum cost network flow
IRIS Incremental Strategy Generation
Exploit Small Support
Attack 1 Attack 2 Attack Attack 6
124 5-10 4-8 hellip -209
378 -8 10 -810 hellip -810
hellip
500 rows
NOT 1041
Attack 1 Attack 2 Attackhellip Attack 6
124 5-10 4-8 hellip -209
Slave (LP Duality Theory)
Master
Converge
GLOBAL
OPTIMAL
Attack 1 Attack 2 Attackhellip Attack 6
124 5-10 4-8 hellip -209
378 -8 10 -810 hellip -810
18
Jain Kiekintveld
59
IRIS Deployed FAMS (2009-)
ldquohellipin 2011 the Military Operations Research Society selected a University
of Southern California project with FAMS on randomizing flight
schedules for the prestigious Rist Awardhelliprdquo
-R S Bray (TSA)
Transportation Security Subcommittee
US House of Representatives 2012
19
Significant change in operations
59
Networks Mumbai Police Checkpoints[2013]
150 edges 2 Checkpoints
150-choose-2 strategies
With V Conitzer 20
Jain
59
Networks Mumbai Police Checkpoints[2013]
Incremental Strategy Generation
With V Conitzer 21
Jain
Double oracle Converge to a global optimal
Path 1 Path 2
Checkpoint
strategy 15 -5 -1 1
Path 1 Path 2
Checkpoint
strategy 15 -5 -1 1
Checkpoint
strategy 2-5 5 2 -1
Defender oracle
Path 1 Path 2 Path 3
Checkpoint
strategy 15 -5 -1 1 -2 2
Checkpoint
strategy 2-5 5 1 -1 -2 2
Attacker oracle
Path 1 Path 2 Path 3
Checkpoint
strategy 15 -5 -1 1 -2 2
Checkpoint
strategy 2-5 5 1 -1 -2 2
59
Double Oracle[2013]Incremental Strategy Generation Exploit Small Support
22
150 edges 2 Checkpoints
Only six candidate edges
for checkpoints
Jain
5923
Mumbai Police Checkpoints[2013]Results of Scale-up
20416 Roads15 checkpoints
20 min
59
Double Oracle Social Cyber Networks[2013]
Incremental Strategy Generation
24
Social networkseg counter-insurgency
Sources
Intermediate
Nodes
Links
Targets
Cyber networks
Tsai
59
Outline ldquoSecurity Gamesrdquo Research
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
25
59
Port Security Threat Scenarios
US Ports $315 trillion economy
USS Cole after suicide attack
French oil tanker hit by small boat
Attack on a ferry
26
59
PROTECT Randomized Patrol Scheduling [2013]
27
Port Protection (Scale-up) and Ferries (Continuous Spacetime)
59
PROTECT Randomized Patrol Scheduling [2013]Port Protection (Scale-up) and Ferries (Continuous Spacetime)
28
59
Ferries Scale-up with Mobile Resources amp Moving TargetsTransition Graph Representation
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
29
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Ferries Scale-up with Mobile Resources amp Moving TargetsTransition Graph Representation
30
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
31
Patrols protect nearby ferry location Solve as done in ARMOR
Ferries Patrol RoutesExponential Numbers of Patrol Routes
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Patrols protect nearby ferry location Solve as done in ARMOR
Ferries Patrol RoutesExponential Numbers of Patrol Routes
32
Pr([(B5) (C 10) (C15)]) = 017
Pr([(B5) (C10) (B15)]) =013
NT variables
Pr([(A5) (A10) (B15)]) = 007
Pr([(A5) (A10) (A15)]) = 003
Jiang KiekintveldFang
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Variables NOT routes but probability flow over each segment
Ferries Scale-up Marginal Probabilities Over Segments
33
NT variables
Jiang KiekintveldFang
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
030
017
013
Obtain marginal probabilities over segments
010
007
003
N2T variablesExtract Pr([(B5) (C 10) (C15)]) = 017
Pr([(B5) (C10) (B15)]) =013
Ferries Scale-up with Marginals Over Separable SegmentsSignificant Speedup
34
NT variables
59
5936
Outline ldquoSecurity Gamesrdquo Research (2007-Now)
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
Evaluation II Real-world deployments
(Patience)
Evaluation I Scale up Handle uncertainty
59
TRUSTS Frequent adversary interaction games
Patrols Against Fare Evaders
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
017
013
010
010
37
59
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
38
Jiang Delle Fave
59
Markov Decision Problems in Security games
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
39
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution Jiang Delle Fave
5940
Urban Transportation Security
MDPs amp DEC-MDPs in Security Games
COPS LA Metro System(Against Opportunistic Crime)
STREETS Singapore Roads(Against Reckless Driving)
DEC-MDPs in Security Games Teamwork + Uncertainty
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
ZhangShieh
59
Uncertainty Space Algorithms
Bayesian and Robust Approaches
Adversary payoff uncertainty
Adversary observation amp
defender execution
uncertainty
Adversary rationality
uncertainty
RECON
BRASS
Monotonic Maximin
(Monotonic adversary)
41
URAC
Payoff interval
Not point
estimateGMC HUNTER
Bayesian
Robust
Yin Nguyen An
0
05
1
15
5 10
Def
end
ers
EU
Targets
ISG RECON URAC
59
Security Games Environmental Crime
amp Bounded Rationality
FisheryGulf of Mexico
Nakai Nam Theun
Forest Area Laos
WildlifeQueen Elizabeth National
ParkUganda
No patrols
Higher
density
Lower densityx
Yang Ford Brown
59
Uncertainty in Adversary Decision Bounded Rationality
Human Subjects as Poachers
59
Uncertainty in Adversary Decision[2009]
Human subjects Anchoring e-Optimality
-4
-3
-2
-1
0
1
Unobserved 5 Observations 20 Observations Unlimited
Average expected reward
DOBSS
Uniform
COBRA
ARMOR
ARMOR Outperforms uniform random
COBRA
MqxCaq
Xxxts
qxR
ji
Xi
ijj
Xi Qj
jiijqx
)1()()1(
|)|1()1(
max
ee
Anchoring
e-optimality
With Sarit Kraus 44
Pita
5945
Quantal Response Stochastic choice better choice more likely
Quantal Response Model of Adversary [2011] Not Maximize Expected Utility
T
j
jxadversary
EU
jxadversary
EU
j
e
eq
1
))((
))((
-3
-25
-2
-15
-1
-05
0
05
1
15
2
Payoff 1 Payoff 2 Payoff 3 Payoff 4
QuantalResponse
COBRA
ARMOR
Adversaryrsquos probability
of choosing target j
Yang
10
)(max1
1
)(
)(
t tt
defendT
jT
j
jxEU
jxEU
x
xKxts
jxEU
e
e
adversary
adversary
59
Uncertainty in Adversary Decision [2012]
Robust vs Modeling Adversaries
Robustness Bound loss to defender Not model attacker via QR
Defeating Robust Learned subjective utility
Robust wins Draw QR wins
42 52 6
Results on 100 games
SU-QR wins Draw Robust
13 8 1
Results on 22 games
MqxCa
qxR
ji
Xi
ij
jiij
Xi Qj
qx
)1()(0
max
β (Adversaryrsquos utility loss if
deviates from optimal) gt=
(Defenderrsquos utility loss due to
adversary deviation)
penaltyattackw
rewardattackw
probcapturewjaSEU
3
2
1)(
M
j
jxSEU
jxSEU
jadversary
adversary
e
eq
1
)(
)(
SU-QR wins Draw Robust
6 13 3
Results against security experts
With Sarit Kraus 46
5947
PAWS Protection Assistant for Wildlife Security[2014]Repeated Stackelberg Game
Simulation
Learn from crime data
Improve model
Defender calculates
mixed strategy
Defender executes
randomized patrols
Poachers attack targets
119890119878119864119880119894(w1w2w3)
119896 119890119878119864119880119896(w1w2w3)
Bayesian SUQR Heterogeneous Poachers
5948
PAWS Test April 2014
Trials in Queen Elizabeth National Park
Andrew Lemieux with rangers
on PAWS patrol in Uganda
59
Security Resource OptimizationEvaluating Deployed Security Systems Not Easy
Game theory Improvement over previous approaches
Previous Human schedulers or ldquosimple randomrdquo
49
Lab
Evaluation
Simulated
adversary
Human subject
adversaries
Field Evaluation
Patrol quality
Unpredictable Cover
Compare real schedules
Scheduling competition
Expert evaluation
Field Evaluation
Tests against adversaries
ldquoMock attackersrdquo
Capture rates of
real adversaries
59
Why Does Game Theory Perform Better
Weaknesses of Previous Methods
Human schedulers
Predictable patterns eg US Coast Guard
Scheduling effort amp cognitive burden
Simple random (eg dice roll)
Wrong weightscoverage eg officers to sparsely crowded terminals
No adversary reactions
Multiple deployments over multiple years without us forcing them
50
59
Lab Evaluation via Simulations
Example from IRIS (FAMS)
-10
-8
-6
-4
-2
0
2
4
6
50 150 250
Defe
nde
r E
xpe
cte
d u
tilit
y
Schedule Size
Uniform Weighted random 1 Weighted random 2 IRIS
51
59
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage
Patrols Before PROTECT Boston Patrols After PROTECT Boston
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Base Patrol Area
52
PROTECT (Coast Guard) 350 increase defender expected utility
59
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage for Less Effort
53
IRIS for FAMS Outperformed expert human over six monthsReportGAO-09-903T
ARMOR at LAX Savings of up to an hour a day in scheduling
59
Field Evaluation Human vs Game Theory Competition
Counter-terrorism Patrol Scheduling
90 officers on LA Metro Trains
Humans required significant effort
Worse schedules than game theory
Observerrsquos report on questions
54
3
35
4
45
5
55
Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Q9
Q10
Q11
Q12
Secu
rity
Sco
re
Human Game Theory
59
Field Test Against Adversaries Mock Attackers
Example from PROTECT
ldquoMock attackerrdquo team deployed in Boston
Comparing PRE- to POST-PROTECT ldquodeterrencerdquo improved
Additional real-world indicators from Boston
Boston boaters questions
ldquohas the Coast Guard recently acquired more boatsrdquo
POST-PROTECT Actual reports of illegal activity
55
5956
Field Tests Against Adversaries
Computational Game Theory in the Field
Game theory vs Random
21 days of patrol
Identical conditions
Random + Human
Not controlled
0
20
40
60
80
100 Miscellaneous
Drugs
Firearm Violations
0
5
10
15
20
Captures30 min
Warnings30 min
Violations30 min
Game Theory
Rand+Human
Controlled
59
Expert Evaluation
Example from ARMOR IRIS amp PROTECT
February 2009 Commendations
LAX Police (City of Los Angeles)
July 2011 Operational Excellence
Award (US Coast Guard Boston)
September 2011 Certificate of
Appreciation (Federal Air Marshals)
June 2013 Meritorious Team Commendation
from Commandant (US Coast Guard)
57
59
Summary Security Games
Decision aids based on computational game theory in daily use
Optimize limited security resources against adversaries
Applications yield research challenges Science of security games
Scale-up Incremental strategy generation amp Marginals
Uncertainty Integrate MDPs Robustness Quantal response
Current applications (wildlife security) Interdisciplinar challenge
Global challenges Merge planninglearning amp security games
58
59
Just the Beginning of ldquoSecurity Gamesrdquohellip
Our next steps
59
Thank you
to sponsors
StartupARMORWAY Game theory in the field
Follow on research amp applications
Privacy audits
(Sinha IJCAIrsquo13)
Software testing
(Kukreja ASErsquo13)
Sport event security
(Yin AAAIrsquo14)
Singapore train
(Varakantham IAAIrsquo13)
Exam questions
(Li IJCAIrsquo13)
59
THANK YOU
tambeuscedu
httpteamcoreuscedusecurity
60
859
Ports amp port traffic
US Coast Guard
Applications Deployed Security Assistants
Airports access
roads amp flights
TSA
Airport Police
Urban transport
LA SheriffrsquosTSA
Singapore Police
Environment
US Coast Guard
WWF WCShellip
59
Key Lessons Security Games
Decision aids based on computational game theory in daily use
Optimize limited security resources against adversaries
Applications yield research challenges Science of security games
Scale-up Incremental strategy generation amp Marginals
Uncertainty Integrate MDPs Robustness Quantal response
Current applications (wildlife security) Interdisciplinary challenge
Global challenges Merge planninglearning amp security games
9
5910
Outline ldquoSecurity Gamesrdquo Research (2007-Now)
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
Evaluation II Real-world deployments
(Patience)
Evaluation I Scale up Handle uncertainty
Publications
AAMAS AAAI IJCAIhellip
2007 onwards
5911
bull 6 plots against LAX
ARMOR LAX (2007) GUARDS TSA (2011)
Airport Security Mapping to Stackelberg Games
GLASGOW 63007
5912
ARMOR Operation [2007]Generate Detailed Defender Schedule Pita Paruchuri
Mixed Integer Program
Pr(Canine patrol 8 AM Terminals 357) = 033
Pr(Canine patrol 8 AM Terminals 256) = 017
helliphellipCanine Team Schedule July 28Term 1 Term 2 Term 3 Term 4 Term 5 Term 6 Term 7 Term 8
8 AM Team1 Team3 Team5
9 AM Team1 Team2 Team4
10 AM Team3 Team5 Team2
Target 1 Target 2
Defender 1 2 -1 -3 4
Defender 2 -3 3 3 -2
5913
ARMOR MIP [2007]Generate Mixed Strategy for Defender
1 i
ixts
jq
ix
ijR
Xi Qj
max
1Qj
jq
MqxCa ji
Xi
ij )1()(0
Maximize defender
expected utility
Defender mixed
strategy
Adversary best
response
Pita Paruchuri
Target 1 Target 2
Defender 1 2 -1 -3 4
Defender 2 -3 3 3 -2
Adversary response
5914
ARMOR Payoffs [2007]Previous Research Provides Payoffs in Security Game Domains
Target 1 Target 2
Defender 1 2 -1 -3 4
Defender 2 -3 3 3 -2
jq
ix
ijR
Xi Qj
maxMaximize defender
expected utility
5915
ARMOR MIP [2007]Solving for a Single Adversary Type
Term 1 Term 2
Defend1 2 -1 -3 4
Defend2 -3 1 3 -3
1 i
ixts
jq
ix
ijR
Xi Qj
max
1Qj
jq
ljq][ix
MqxCa ji
Xi
ij
1010
)1()(0
Maximize defender
expected utility
Defender strategy
Adversary strategy
Adversary best
response
119909119894
ARMORhellipthrows a digital cloak of invisibilityhellip
59
IRIS Federal Air Marshals Service [2009]Scale Up Number of Defender Strategies
1000 Flights 20 air marshals 1041
combinations
ARMOR out of memory
Strategy 1 Strategy 2 Strategy 3
Strateg
y 1
Strateg
y 2
Strateg
y 3
Strateg
y 4
Strateg
y 5
Strateg
y 6
Not enumerate all combinations
Branch and price Incremental strategy generation
Strategy 1 Strategy 2 Strategy 3
Strategy 1
Strategy 2
Strategy 3
Strategy 4
Strategy 5
Strategy 6
16
59
IRIS Scale Up Number of Defender Strategies [2009]
Small Support Set for Mixed Strategies
Small support set size
bull Most xi variables zero
x124=0239
x123=00
x135=00
Attack
1
Attack
2
Attack
hellip
Attack
1000
123 5-10 4-8 hellip -209
124 5-10 4-8 hellip -209
135 5-10 -95 hellip -209
hellip
hellip1041 rowsx378=0123
10]10[
)1()(0
11
max
jqx
MqxCa
qxts
qxR
i
ji
Xi
ij
Qj
j
i
i
jiij
Xi Qj
qx
1000 flights 20 air marshals
1041combinations
17
59
Target 3 Target 7
hellip hellip
Resource Sink
Best new pure strategy
Minimum cost network flow
IRIS Incremental Strategy Generation
Exploit Small Support
Attack 1 Attack 2 Attack Attack 6
124 5-10 4-8 hellip -209
378 -8 10 -810 hellip -810
hellip
500 rows
NOT 1041
Attack 1 Attack 2 Attackhellip Attack 6
124 5-10 4-8 hellip -209
Slave (LP Duality Theory)
Master
Converge
GLOBAL
OPTIMAL
Attack 1 Attack 2 Attackhellip Attack 6
124 5-10 4-8 hellip -209
378 -8 10 -810 hellip -810
18
Jain Kiekintveld
59
IRIS Deployed FAMS (2009-)
ldquohellipin 2011 the Military Operations Research Society selected a University
of Southern California project with FAMS on randomizing flight
schedules for the prestigious Rist Awardhelliprdquo
-R S Bray (TSA)
Transportation Security Subcommittee
US House of Representatives 2012
19
Significant change in operations
59
Networks Mumbai Police Checkpoints[2013]
150 edges 2 Checkpoints
150-choose-2 strategies
With V Conitzer 20
Jain
59
Networks Mumbai Police Checkpoints[2013]
Incremental Strategy Generation
With V Conitzer 21
Jain
Double oracle Converge to a global optimal
Path 1 Path 2
Checkpoint
strategy 15 -5 -1 1
Path 1 Path 2
Checkpoint
strategy 15 -5 -1 1
Checkpoint
strategy 2-5 5 2 -1
Defender oracle
Path 1 Path 2 Path 3
Checkpoint
strategy 15 -5 -1 1 -2 2
Checkpoint
strategy 2-5 5 1 -1 -2 2
Attacker oracle
Path 1 Path 2 Path 3
Checkpoint
strategy 15 -5 -1 1 -2 2
Checkpoint
strategy 2-5 5 1 -1 -2 2
59
Double Oracle[2013]Incremental Strategy Generation Exploit Small Support
22
150 edges 2 Checkpoints
Only six candidate edges
for checkpoints
Jain
5923
Mumbai Police Checkpoints[2013]Results of Scale-up
20416 Roads15 checkpoints
20 min
59
Double Oracle Social Cyber Networks[2013]
Incremental Strategy Generation
24
Social networkseg counter-insurgency
Sources
Intermediate
Nodes
Links
Targets
Cyber networks
Tsai
59
Outline ldquoSecurity Gamesrdquo Research
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
25
59
Port Security Threat Scenarios
US Ports $315 trillion economy
USS Cole after suicide attack
French oil tanker hit by small boat
Attack on a ferry
26
59
PROTECT Randomized Patrol Scheduling [2013]
27
Port Protection (Scale-up) and Ferries (Continuous Spacetime)
59
PROTECT Randomized Patrol Scheduling [2013]Port Protection (Scale-up) and Ferries (Continuous Spacetime)
28
59
Ferries Scale-up with Mobile Resources amp Moving TargetsTransition Graph Representation
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
29
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Ferries Scale-up with Mobile Resources amp Moving TargetsTransition Graph Representation
30
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
31
Patrols protect nearby ferry location Solve as done in ARMOR
Ferries Patrol RoutesExponential Numbers of Patrol Routes
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Patrols protect nearby ferry location Solve as done in ARMOR
Ferries Patrol RoutesExponential Numbers of Patrol Routes
32
Pr([(B5) (C 10) (C15)]) = 017
Pr([(B5) (C10) (B15)]) =013
NT variables
Pr([(A5) (A10) (B15)]) = 007
Pr([(A5) (A10) (A15)]) = 003
Jiang KiekintveldFang
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Variables NOT routes but probability flow over each segment
Ferries Scale-up Marginal Probabilities Over Segments
33
NT variables
Jiang KiekintveldFang
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
030
017
013
Obtain marginal probabilities over segments
010
007
003
N2T variablesExtract Pr([(B5) (C 10) (C15)]) = 017
Pr([(B5) (C10) (B15)]) =013
Ferries Scale-up with Marginals Over Separable SegmentsSignificant Speedup
34
NT variables
59
5936
Outline ldquoSecurity Gamesrdquo Research (2007-Now)
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
Evaluation II Real-world deployments
(Patience)
Evaluation I Scale up Handle uncertainty
59
TRUSTS Frequent adversary interaction games
Patrols Against Fare Evaders
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
017
013
010
010
37
59
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
38
Jiang Delle Fave
59
Markov Decision Problems in Security games
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
39
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution Jiang Delle Fave
5940
Urban Transportation Security
MDPs amp DEC-MDPs in Security Games
COPS LA Metro System(Against Opportunistic Crime)
STREETS Singapore Roads(Against Reckless Driving)
DEC-MDPs in Security Games Teamwork + Uncertainty
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
ZhangShieh
59
Uncertainty Space Algorithms
Bayesian and Robust Approaches
Adversary payoff uncertainty
Adversary observation amp
defender execution
uncertainty
Adversary rationality
uncertainty
RECON
BRASS
Monotonic Maximin
(Monotonic adversary)
41
URAC
Payoff interval
Not point
estimateGMC HUNTER
Bayesian
Robust
Yin Nguyen An
0
05
1
15
5 10
Def
end
ers
EU
Targets
ISG RECON URAC
59
Security Games Environmental Crime
amp Bounded Rationality
FisheryGulf of Mexico
Nakai Nam Theun
Forest Area Laos
WildlifeQueen Elizabeth National
ParkUganda
No patrols
Higher
density
Lower densityx
Yang Ford Brown
59
Uncertainty in Adversary Decision Bounded Rationality
Human Subjects as Poachers
59
Uncertainty in Adversary Decision[2009]
Human subjects Anchoring e-Optimality
-4
-3
-2
-1
0
1
Unobserved 5 Observations 20 Observations Unlimited
Average expected reward
DOBSS
Uniform
COBRA
ARMOR
ARMOR Outperforms uniform random
COBRA
MqxCaq
Xxxts
qxR
ji
Xi
ijj
Xi Qj
jiijqx
)1()()1(
|)|1()1(
max
ee
Anchoring
e-optimality
With Sarit Kraus 44
Pita
5945
Quantal Response Stochastic choice better choice more likely
Quantal Response Model of Adversary [2011] Not Maximize Expected Utility
T
j
jxadversary
EU
jxadversary
EU
j
e
eq
1
))((
))((
-3
-25
-2
-15
-1
-05
0
05
1
15
2
Payoff 1 Payoff 2 Payoff 3 Payoff 4
QuantalResponse
COBRA
ARMOR
Adversaryrsquos probability
of choosing target j
Yang
10
)(max1
1
)(
)(
t tt
defendT
jT
j
jxEU
jxEU
x
xKxts
jxEU
e
e
adversary
adversary
59
Uncertainty in Adversary Decision [2012]
Robust vs Modeling Adversaries
Robustness Bound loss to defender Not model attacker via QR
Defeating Robust Learned subjective utility
Robust wins Draw QR wins
42 52 6
Results on 100 games
SU-QR wins Draw Robust
13 8 1
Results on 22 games
MqxCa
qxR
ji
Xi
ij
jiij
Xi Qj
qx
)1()(0
max
β (Adversaryrsquos utility loss if
deviates from optimal) gt=
(Defenderrsquos utility loss due to
adversary deviation)
penaltyattackw
rewardattackw
probcapturewjaSEU
3
2
1)(
M
j
jxSEU
jxSEU
jadversary
adversary
e
eq
1
)(
)(
SU-QR wins Draw Robust
6 13 3
Results against security experts
With Sarit Kraus 46
5947
PAWS Protection Assistant for Wildlife Security[2014]Repeated Stackelberg Game
Simulation
Learn from crime data
Improve model
Defender calculates
mixed strategy
Defender executes
randomized patrols
Poachers attack targets
119890119878119864119880119894(w1w2w3)
119896 119890119878119864119880119896(w1w2w3)
Bayesian SUQR Heterogeneous Poachers
5948
PAWS Test April 2014
Trials in Queen Elizabeth National Park
Andrew Lemieux with rangers
on PAWS patrol in Uganda
59
Security Resource OptimizationEvaluating Deployed Security Systems Not Easy
Game theory Improvement over previous approaches
Previous Human schedulers or ldquosimple randomrdquo
49
Lab
Evaluation
Simulated
adversary
Human subject
adversaries
Field Evaluation
Patrol quality
Unpredictable Cover
Compare real schedules
Scheduling competition
Expert evaluation
Field Evaluation
Tests against adversaries
ldquoMock attackersrdquo
Capture rates of
real adversaries
59
Why Does Game Theory Perform Better
Weaknesses of Previous Methods
Human schedulers
Predictable patterns eg US Coast Guard
Scheduling effort amp cognitive burden
Simple random (eg dice roll)
Wrong weightscoverage eg officers to sparsely crowded terminals
No adversary reactions
Multiple deployments over multiple years without us forcing them
50
59
Lab Evaluation via Simulations
Example from IRIS (FAMS)
-10
-8
-6
-4
-2
0
2
4
6
50 150 250
Defe
nde
r E
xpe
cte
d u
tilit
y
Schedule Size
Uniform Weighted random 1 Weighted random 2 IRIS
51
59
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage
Patrols Before PROTECT Boston Patrols After PROTECT Boston
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Base Patrol Area
52
PROTECT (Coast Guard) 350 increase defender expected utility
59
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage for Less Effort
53
IRIS for FAMS Outperformed expert human over six monthsReportGAO-09-903T
ARMOR at LAX Savings of up to an hour a day in scheduling
59
Field Evaluation Human vs Game Theory Competition
Counter-terrorism Patrol Scheduling
90 officers on LA Metro Trains
Humans required significant effort
Worse schedules than game theory
Observerrsquos report on questions
54
3
35
4
45
5
55
Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Q9
Q10
Q11
Q12
Secu
rity
Sco
re
Human Game Theory
59
Field Test Against Adversaries Mock Attackers
Example from PROTECT
ldquoMock attackerrdquo team deployed in Boston
Comparing PRE- to POST-PROTECT ldquodeterrencerdquo improved
Additional real-world indicators from Boston
Boston boaters questions
ldquohas the Coast Guard recently acquired more boatsrdquo
POST-PROTECT Actual reports of illegal activity
55
5956
Field Tests Against Adversaries
Computational Game Theory in the Field
Game theory vs Random
21 days of patrol
Identical conditions
Random + Human
Not controlled
0
20
40
60
80
100 Miscellaneous
Drugs
Firearm Violations
0
5
10
15
20
Captures30 min
Warnings30 min
Violations30 min
Game Theory
Rand+Human
Controlled
59
Expert Evaluation
Example from ARMOR IRIS amp PROTECT
February 2009 Commendations
LAX Police (City of Los Angeles)
July 2011 Operational Excellence
Award (US Coast Guard Boston)
September 2011 Certificate of
Appreciation (Federal Air Marshals)
June 2013 Meritorious Team Commendation
from Commandant (US Coast Guard)
57
59
Summary Security Games
Decision aids based on computational game theory in daily use
Optimize limited security resources against adversaries
Applications yield research challenges Science of security games
Scale-up Incremental strategy generation amp Marginals
Uncertainty Integrate MDPs Robustness Quantal response
Current applications (wildlife security) Interdisciplinar challenge
Global challenges Merge planninglearning amp security games
58
59
Just the Beginning of ldquoSecurity Gamesrdquohellip
Our next steps
59
Thank you
to sponsors
StartupARMORWAY Game theory in the field
Follow on research amp applications
Privacy audits
(Sinha IJCAIrsquo13)
Software testing
(Kukreja ASErsquo13)
Sport event security
(Yin AAAIrsquo14)
Singapore train
(Varakantham IAAIrsquo13)
Exam questions
(Li IJCAIrsquo13)
59
THANK YOU
tambeuscedu
httpteamcoreuscedusecurity
60
59
Key Lessons Security Games
Decision aids based on computational game theory in daily use
Optimize limited security resources against adversaries
Applications yield research challenges Science of security games
Scale-up Incremental strategy generation amp Marginals
Uncertainty Integrate MDPs Robustness Quantal response
Current applications (wildlife security) Interdisciplinary challenge
Global challenges Merge planninglearning amp security games
9
5910
Outline ldquoSecurity Gamesrdquo Research (2007-Now)
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
Evaluation II Real-world deployments
(Patience)
Evaluation I Scale up Handle uncertainty
Publications
AAMAS AAAI IJCAIhellip
2007 onwards
5911
bull 6 plots against LAX
ARMOR LAX (2007) GUARDS TSA (2011)
Airport Security Mapping to Stackelberg Games
GLASGOW 63007
5912
ARMOR Operation [2007]Generate Detailed Defender Schedule Pita Paruchuri
Mixed Integer Program
Pr(Canine patrol 8 AM Terminals 357) = 033
Pr(Canine patrol 8 AM Terminals 256) = 017
helliphellipCanine Team Schedule July 28Term 1 Term 2 Term 3 Term 4 Term 5 Term 6 Term 7 Term 8
8 AM Team1 Team3 Team5
9 AM Team1 Team2 Team4
10 AM Team3 Team5 Team2
Target 1 Target 2
Defender 1 2 -1 -3 4
Defender 2 -3 3 3 -2
5913
ARMOR MIP [2007]Generate Mixed Strategy for Defender
1 i
ixts
jq
ix
ijR
Xi Qj
max
1Qj
jq
MqxCa ji
Xi
ij )1()(0
Maximize defender
expected utility
Defender mixed
strategy
Adversary best
response
Pita Paruchuri
Target 1 Target 2
Defender 1 2 -1 -3 4
Defender 2 -3 3 3 -2
Adversary response
5914
ARMOR Payoffs [2007]Previous Research Provides Payoffs in Security Game Domains
Target 1 Target 2
Defender 1 2 -1 -3 4
Defender 2 -3 3 3 -2
jq
ix
ijR
Xi Qj
maxMaximize defender
expected utility
5915
ARMOR MIP [2007]Solving for a Single Adversary Type
Term 1 Term 2
Defend1 2 -1 -3 4
Defend2 -3 1 3 -3
1 i
ixts
jq
ix
ijR
Xi Qj
max
1Qj
jq
ljq][ix
MqxCa ji
Xi
ij
1010
)1()(0
Maximize defender
expected utility
Defender strategy
Adversary strategy
Adversary best
response
119909119894
ARMORhellipthrows a digital cloak of invisibilityhellip
59
IRIS Federal Air Marshals Service [2009]Scale Up Number of Defender Strategies
1000 Flights 20 air marshals 1041
combinations
ARMOR out of memory
Strategy 1 Strategy 2 Strategy 3
Strateg
y 1
Strateg
y 2
Strateg
y 3
Strateg
y 4
Strateg
y 5
Strateg
y 6
Not enumerate all combinations
Branch and price Incremental strategy generation
Strategy 1 Strategy 2 Strategy 3
Strategy 1
Strategy 2
Strategy 3
Strategy 4
Strategy 5
Strategy 6
16
59
IRIS Scale Up Number of Defender Strategies [2009]
Small Support Set for Mixed Strategies
Small support set size
bull Most xi variables zero
x124=0239
x123=00
x135=00
Attack
1
Attack
2
Attack
hellip
Attack
1000
123 5-10 4-8 hellip -209
124 5-10 4-8 hellip -209
135 5-10 -95 hellip -209
hellip
hellip1041 rowsx378=0123
10]10[
)1()(0
11
max
jqx
MqxCa
qxts
qxR
i
ji
Xi
ij
Qj
j
i
i
jiij
Xi Qj
qx
1000 flights 20 air marshals
1041combinations
17
59
Target 3 Target 7
hellip hellip
Resource Sink
Best new pure strategy
Minimum cost network flow
IRIS Incremental Strategy Generation
Exploit Small Support
Attack 1 Attack 2 Attack Attack 6
124 5-10 4-8 hellip -209
378 -8 10 -810 hellip -810
hellip
500 rows
NOT 1041
Attack 1 Attack 2 Attackhellip Attack 6
124 5-10 4-8 hellip -209
Slave (LP Duality Theory)
Master
Converge
GLOBAL
OPTIMAL
Attack 1 Attack 2 Attackhellip Attack 6
124 5-10 4-8 hellip -209
378 -8 10 -810 hellip -810
18
Jain Kiekintveld
59
IRIS Deployed FAMS (2009-)
ldquohellipin 2011 the Military Operations Research Society selected a University
of Southern California project with FAMS on randomizing flight
schedules for the prestigious Rist Awardhelliprdquo
-R S Bray (TSA)
Transportation Security Subcommittee
US House of Representatives 2012
19
Significant change in operations
59
Networks Mumbai Police Checkpoints[2013]
150 edges 2 Checkpoints
150-choose-2 strategies
With V Conitzer 20
Jain
59
Networks Mumbai Police Checkpoints[2013]
Incremental Strategy Generation
With V Conitzer 21
Jain
Double oracle Converge to a global optimal
Path 1 Path 2
Checkpoint
strategy 15 -5 -1 1
Path 1 Path 2
Checkpoint
strategy 15 -5 -1 1
Checkpoint
strategy 2-5 5 2 -1
Defender oracle
Path 1 Path 2 Path 3
Checkpoint
strategy 15 -5 -1 1 -2 2
Checkpoint
strategy 2-5 5 1 -1 -2 2
Attacker oracle
Path 1 Path 2 Path 3
Checkpoint
strategy 15 -5 -1 1 -2 2
Checkpoint
strategy 2-5 5 1 -1 -2 2
59
Double Oracle[2013]Incremental Strategy Generation Exploit Small Support
22
150 edges 2 Checkpoints
Only six candidate edges
for checkpoints
Jain
5923
Mumbai Police Checkpoints[2013]Results of Scale-up
20416 Roads15 checkpoints
20 min
59
Double Oracle Social Cyber Networks[2013]
Incremental Strategy Generation
24
Social networkseg counter-insurgency
Sources
Intermediate
Nodes
Links
Targets
Cyber networks
Tsai
59
Outline ldquoSecurity Gamesrdquo Research
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
25
59
Port Security Threat Scenarios
US Ports $315 trillion economy
USS Cole after suicide attack
French oil tanker hit by small boat
Attack on a ferry
26
59
PROTECT Randomized Patrol Scheduling [2013]
27
Port Protection (Scale-up) and Ferries (Continuous Spacetime)
59
PROTECT Randomized Patrol Scheduling [2013]Port Protection (Scale-up) and Ferries (Continuous Spacetime)
28
59
Ferries Scale-up with Mobile Resources amp Moving TargetsTransition Graph Representation
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
29
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Ferries Scale-up with Mobile Resources amp Moving TargetsTransition Graph Representation
30
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
31
Patrols protect nearby ferry location Solve as done in ARMOR
Ferries Patrol RoutesExponential Numbers of Patrol Routes
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Patrols protect nearby ferry location Solve as done in ARMOR
Ferries Patrol RoutesExponential Numbers of Patrol Routes
32
Pr([(B5) (C 10) (C15)]) = 017
Pr([(B5) (C10) (B15)]) =013
NT variables
Pr([(A5) (A10) (B15)]) = 007
Pr([(A5) (A10) (A15)]) = 003
Jiang KiekintveldFang
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Variables NOT routes but probability flow over each segment
Ferries Scale-up Marginal Probabilities Over Segments
33
NT variables
Jiang KiekintveldFang
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
030
017
013
Obtain marginal probabilities over segments
010
007
003
N2T variablesExtract Pr([(B5) (C 10) (C15)]) = 017
Pr([(B5) (C10) (B15)]) =013
Ferries Scale-up with Marginals Over Separable SegmentsSignificant Speedup
34
NT variables
59
5936
Outline ldquoSecurity Gamesrdquo Research (2007-Now)
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
Evaluation II Real-world deployments
(Patience)
Evaluation I Scale up Handle uncertainty
59
TRUSTS Frequent adversary interaction games
Patrols Against Fare Evaders
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
017
013
010
010
37
59
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
38
Jiang Delle Fave
59
Markov Decision Problems in Security games
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
39
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution Jiang Delle Fave
5940
Urban Transportation Security
MDPs amp DEC-MDPs in Security Games
COPS LA Metro System(Against Opportunistic Crime)
STREETS Singapore Roads(Against Reckless Driving)
DEC-MDPs in Security Games Teamwork + Uncertainty
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
ZhangShieh
59
Uncertainty Space Algorithms
Bayesian and Robust Approaches
Adversary payoff uncertainty
Adversary observation amp
defender execution
uncertainty
Adversary rationality
uncertainty
RECON
BRASS
Monotonic Maximin
(Monotonic adversary)
41
URAC
Payoff interval
Not point
estimateGMC HUNTER
Bayesian
Robust
Yin Nguyen An
0
05
1
15
5 10
Def
end
ers
EU
Targets
ISG RECON URAC
59
Security Games Environmental Crime
amp Bounded Rationality
FisheryGulf of Mexico
Nakai Nam Theun
Forest Area Laos
WildlifeQueen Elizabeth National
ParkUganda
No patrols
Higher
density
Lower densityx
Yang Ford Brown
59
Uncertainty in Adversary Decision Bounded Rationality
Human Subjects as Poachers
59
Uncertainty in Adversary Decision[2009]
Human subjects Anchoring e-Optimality
-4
-3
-2
-1
0
1
Unobserved 5 Observations 20 Observations Unlimited
Average expected reward
DOBSS
Uniform
COBRA
ARMOR
ARMOR Outperforms uniform random
COBRA
MqxCaq
Xxxts
qxR
ji
Xi
ijj
Xi Qj
jiijqx
)1()()1(
|)|1()1(
max
ee
Anchoring
e-optimality
With Sarit Kraus 44
Pita
5945
Quantal Response Stochastic choice better choice more likely
Quantal Response Model of Adversary [2011] Not Maximize Expected Utility
T
j
jxadversary
EU
jxadversary
EU
j
e
eq
1
))((
))((
-3
-25
-2
-15
-1
-05
0
05
1
15
2
Payoff 1 Payoff 2 Payoff 3 Payoff 4
QuantalResponse
COBRA
ARMOR
Adversaryrsquos probability
of choosing target j
Yang
10
)(max1
1
)(
)(
t tt
defendT
jT
j
jxEU
jxEU
x
xKxts
jxEU
e
e
adversary
adversary
59
Uncertainty in Adversary Decision [2012]
Robust vs Modeling Adversaries
Robustness Bound loss to defender Not model attacker via QR
Defeating Robust Learned subjective utility
Robust wins Draw QR wins
42 52 6
Results on 100 games
SU-QR wins Draw Robust
13 8 1
Results on 22 games
MqxCa
qxR
ji
Xi
ij
jiij
Xi Qj
qx
)1()(0
max
β (Adversaryrsquos utility loss if
deviates from optimal) gt=
(Defenderrsquos utility loss due to
adversary deviation)
penaltyattackw
rewardattackw
probcapturewjaSEU
3
2
1)(
M
j
jxSEU
jxSEU
jadversary
adversary
e
eq
1
)(
)(
SU-QR wins Draw Robust
6 13 3
Results against security experts
With Sarit Kraus 46
5947
PAWS Protection Assistant for Wildlife Security[2014]Repeated Stackelberg Game
Simulation
Learn from crime data
Improve model
Defender calculates
mixed strategy
Defender executes
randomized patrols
Poachers attack targets
119890119878119864119880119894(w1w2w3)
119896 119890119878119864119880119896(w1w2w3)
Bayesian SUQR Heterogeneous Poachers
5948
PAWS Test April 2014
Trials in Queen Elizabeth National Park
Andrew Lemieux with rangers
on PAWS patrol in Uganda
59
Security Resource OptimizationEvaluating Deployed Security Systems Not Easy
Game theory Improvement over previous approaches
Previous Human schedulers or ldquosimple randomrdquo
49
Lab
Evaluation
Simulated
adversary
Human subject
adversaries
Field Evaluation
Patrol quality
Unpredictable Cover
Compare real schedules
Scheduling competition
Expert evaluation
Field Evaluation
Tests against adversaries
ldquoMock attackersrdquo
Capture rates of
real adversaries
59
Why Does Game Theory Perform Better
Weaknesses of Previous Methods
Human schedulers
Predictable patterns eg US Coast Guard
Scheduling effort amp cognitive burden
Simple random (eg dice roll)
Wrong weightscoverage eg officers to sparsely crowded terminals
No adversary reactions
Multiple deployments over multiple years without us forcing them
50
59
Lab Evaluation via Simulations
Example from IRIS (FAMS)
-10
-8
-6
-4
-2
0
2
4
6
50 150 250
Defe
nde
r E
xpe
cte
d u
tilit
y
Schedule Size
Uniform Weighted random 1 Weighted random 2 IRIS
51
59
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage
Patrols Before PROTECT Boston Patrols After PROTECT Boston
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Base Patrol Area
52
PROTECT (Coast Guard) 350 increase defender expected utility
59
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage for Less Effort
53
IRIS for FAMS Outperformed expert human over six monthsReportGAO-09-903T
ARMOR at LAX Savings of up to an hour a day in scheduling
59
Field Evaluation Human vs Game Theory Competition
Counter-terrorism Patrol Scheduling
90 officers on LA Metro Trains
Humans required significant effort
Worse schedules than game theory
Observerrsquos report on questions
54
3
35
4
45
5
55
Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Q9
Q10
Q11
Q12
Secu
rity
Sco
re
Human Game Theory
59
Field Test Against Adversaries Mock Attackers
Example from PROTECT
ldquoMock attackerrdquo team deployed in Boston
Comparing PRE- to POST-PROTECT ldquodeterrencerdquo improved
Additional real-world indicators from Boston
Boston boaters questions
ldquohas the Coast Guard recently acquired more boatsrdquo
POST-PROTECT Actual reports of illegal activity
55
5956
Field Tests Against Adversaries
Computational Game Theory in the Field
Game theory vs Random
21 days of patrol
Identical conditions
Random + Human
Not controlled
0
20
40
60
80
100 Miscellaneous
Drugs
Firearm Violations
0
5
10
15
20
Captures30 min
Warnings30 min
Violations30 min
Game Theory
Rand+Human
Controlled
59
Expert Evaluation
Example from ARMOR IRIS amp PROTECT
February 2009 Commendations
LAX Police (City of Los Angeles)
July 2011 Operational Excellence
Award (US Coast Guard Boston)
September 2011 Certificate of
Appreciation (Federal Air Marshals)
June 2013 Meritorious Team Commendation
from Commandant (US Coast Guard)
57
59
Summary Security Games
Decision aids based on computational game theory in daily use
Optimize limited security resources against adversaries
Applications yield research challenges Science of security games
Scale-up Incremental strategy generation amp Marginals
Uncertainty Integrate MDPs Robustness Quantal response
Current applications (wildlife security) Interdisciplinar challenge
Global challenges Merge planninglearning amp security games
58
59
Just the Beginning of ldquoSecurity Gamesrdquohellip
Our next steps
59
Thank you
to sponsors
StartupARMORWAY Game theory in the field
Follow on research amp applications
Privacy audits
(Sinha IJCAIrsquo13)
Software testing
(Kukreja ASErsquo13)
Sport event security
(Yin AAAIrsquo14)
Singapore train
(Varakantham IAAIrsquo13)
Exam questions
(Li IJCAIrsquo13)
59
THANK YOU
tambeuscedu
httpteamcoreuscedusecurity
60
5910
Outline ldquoSecurity Gamesrdquo Research (2007-Now)
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
Evaluation II Real-world deployments
(Patience)
Evaluation I Scale up Handle uncertainty
Publications
AAMAS AAAI IJCAIhellip
2007 onwards
5911
bull 6 plots against LAX
ARMOR LAX (2007) GUARDS TSA (2011)
Airport Security Mapping to Stackelberg Games
GLASGOW 63007
5912
ARMOR Operation [2007]Generate Detailed Defender Schedule Pita Paruchuri
Mixed Integer Program
Pr(Canine patrol 8 AM Terminals 357) = 033
Pr(Canine patrol 8 AM Terminals 256) = 017
helliphellipCanine Team Schedule July 28Term 1 Term 2 Term 3 Term 4 Term 5 Term 6 Term 7 Term 8
8 AM Team1 Team3 Team5
9 AM Team1 Team2 Team4
10 AM Team3 Team5 Team2
Target 1 Target 2
Defender 1 2 -1 -3 4
Defender 2 -3 3 3 -2
5913
ARMOR MIP [2007]Generate Mixed Strategy for Defender
1 i
ixts
jq
ix
ijR
Xi Qj
max
1Qj
jq
MqxCa ji
Xi
ij )1()(0
Maximize defender
expected utility
Defender mixed
strategy
Adversary best
response
Pita Paruchuri
Target 1 Target 2
Defender 1 2 -1 -3 4
Defender 2 -3 3 3 -2
Adversary response
5914
ARMOR Payoffs [2007]Previous Research Provides Payoffs in Security Game Domains
Target 1 Target 2
Defender 1 2 -1 -3 4
Defender 2 -3 3 3 -2
jq
ix
ijR
Xi Qj
maxMaximize defender
expected utility
5915
ARMOR MIP [2007]Solving for a Single Adversary Type
Term 1 Term 2
Defend1 2 -1 -3 4
Defend2 -3 1 3 -3
1 i
ixts
jq
ix
ijR
Xi Qj
max
1Qj
jq
ljq][ix
MqxCa ji
Xi
ij
1010
)1()(0
Maximize defender
expected utility
Defender strategy
Adversary strategy
Adversary best
response
119909119894
ARMORhellipthrows a digital cloak of invisibilityhellip
59
IRIS Federal Air Marshals Service [2009]Scale Up Number of Defender Strategies
1000 Flights 20 air marshals 1041
combinations
ARMOR out of memory
Strategy 1 Strategy 2 Strategy 3
Strateg
y 1
Strateg
y 2
Strateg
y 3
Strateg
y 4
Strateg
y 5
Strateg
y 6
Not enumerate all combinations
Branch and price Incremental strategy generation
Strategy 1 Strategy 2 Strategy 3
Strategy 1
Strategy 2
Strategy 3
Strategy 4
Strategy 5
Strategy 6
16
59
IRIS Scale Up Number of Defender Strategies [2009]
Small Support Set for Mixed Strategies
Small support set size
bull Most xi variables zero
x124=0239
x123=00
x135=00
Attack
1
Attack
2
Attack
hellip
Attack
1000
123 5-10 4-8 hellip -209
124 5-10 4-8 hellip -209
135 5-10 -95 hellip -209
hellip
hellip1041 rowsx378=0123
10]10[
)1()(0
11
max
jqx
MqxCa
qxts
qxR
i
ji
Xi
ij
Qj
j
i
i
jiij
Xi Qj
qx
1000 flights 20 air marshals
1041combinations
17
59
Target 3 Target 7
hellip hellip
Resource Sink
Best new pure strategy
Minimum cost network flow
IRIS Incremental Strategy Generation
Exploit Small Support
Attack 1 Attack 2 Attack Attack 6
124 5-10 4-8 hellip -209
378 -8 10 -810 hellip -810
hellip
500 rows
NOT 1041
Attack 1 Attack 2 Attackhellip Attack 6
124 5-10 4-8 hellip -209
Slave (LP Duality Theory)
Master
Converge
GLOBAL
OPTIMAL
Attack 1 Attack 2 Attackhellip Attack 6
124 5-10 4-8 hellip -209
378 -8 10 -810 hellip -810
18
Jain Kiekintveld
59
IRIS Deployed FAMS (2009-)
ldquohellipin 2011 the Military Operations Research Society selected a University
of Southern California project with FAMS on randomizing flight
schedules for the prestigious Rist Awardhelliprdquo
-R S Bray (TSA)
Transportation Security Subcommittee
US House of Representatives 2012
19
Significant change in operations
59
Networks Mumbai Police Checkpoints[2013]
150 edges 2 Checkpoints
150-choose-2 strategies
With V Conitzer 20
Jain
59
Networks Mumbai Police Checkpoints[2013]
Incremental Strategy Generation
With V Conitzer 21
Jain
Double oracle Converge to a global optimal
Path 1 Path 2
Checkpoint
strategy 15 -5 -1 1
Path 1 Path 2
Checkpoint
strategy 15 -5 -1 1
Checkpoint
strategy 2-5 5 2 -1
Defender oracle
Path 1 Path 2 Path 3
Checkpoint
strategy 15 -5 -1 1 -2 2
Checkpoint
strategy 2-5 5 1 -1 -2 2
Attacker oracle
Path 1 Path 2 Path 3
Checkpoint
strategy 15 -5 -1 1 -2 2
Checkpoint
strategy 2-5 5 1 -1 -2 2
59
Double Oracle[2013]Incremental Strategy Generation Exploit Small Support
22
150 edges 2 Checkpoints
Only six candidate edges
for checkpoints
Jain
5923
Mumbai Police Checkpoints[2013]Results of Scale-up
20416 Roads15 checkpoints
20 min
59
Double Oracle Social Cyber Networks[2013]
Incremental Strategy Generation
24
Social networkseg counter-insurgency
Sources
Intermediate
Nodes
Links
Targets
Cyber networks
Tsai
59
Outline ldquoSecurity Gamesrdquo Research
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
25
59
Port Security Threat Scenarios
US Ports $315 trillion economy
USS Cole after suicide attack
French oil tanker hit by small boat
Attack on a ferry
26
59
PROTECT Randomized Patrol Scheduling [2013]
27
Port Protection (Scale-up) and Ferries (Continuous Spacetime)
59
PROTECT Randomized Patrol Scheduling [2013]Port Protection (Scale-up) and Ferries (Continuous Spacetime)
28
59
Ferries Scale-up with Mobile Resources amp Moving TargetsTransition Graph Representation
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
29
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Ferries Scale-up with Mobile Resources amp Moving TargetsTransition Graph Representation
30
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
31
Patrols protect nearby ferry location Solve as done in ARMOR
Ferries Patrol RoutesExponential Numbers of Patrol Routes
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Patrols protect nearby ferry location Solve as done in ARMOR
Ferries Patrol RoutesExponential Numbers of Patrol Routes
32
Pr([(B5) (C 10) (C15)]) = 017
Pr([(B5) (C10) (B15)]) =013
NT variables
Pr([(A5) (A10) (B15)]) = 007
Pr([(A5) (A10) (A15)]) = 003
Jiang KiekintveldFang
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Variables NOT routes but probability flow over each segment
Ferries Scale-up Marginal Probabilities Over Segments
33
NT variables
Jiang KiekintveldFang
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
030
017
013
Obtain marginal probabilities over segments
010
007
003
N2T variablesExtract Pr([(B5) (C 10) (C15)]) = 017
Pr([(B5) (C10) (B15)]) =013
Ferries Scale-up with Marginals Over Separable SegmentsSignificant Speedup
34
NT variables
59
5936
Outline ldquoSecurity Gamesrdquo Research (2007-Now)
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
Evaluation II Real-world deployments
(Patience)
Evaluation I Scale up Handle uncertainty
59
TRUSTS Frequent adversary interaction games
Patrols Against Fare Evaders
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
017
013
010
010
37
59
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
38
Jiang Delle Fave
59
Markov Decision Problems in Security games
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
39
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution Jiang Delle Fave
5940
Urban Transportation Security
MDPs amp DEC-MDPs in Security Games
COPS LA Metro System(Against Opportunistic Crime)
STREETS Singapore Roads(Against Reckless Driving)
DEC-MDPs in Security Games Teamwork + Uncertainty
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
ZhangShieh
59
Uncertainty Space Algorithms
Bayesian and Robust Approaches
Adversary payoff uncertainty
Adversary observation amp
defender execution
uncertainty
Adversary rationality
uncertainty
RECON
BRASS
Monotonic Maximin
(Monotonic adversary)
41
URAC
Payoff interval
Not point
estimateGMC HUNTER
Bayesian
Robust
Yin Nguyen An
0
05
1
15
5 10
Def
end
ers
EU
Targets
ISG RECON URAC
59
Security Games Environmental Crime
amp Bounded Rationality
FisheryGulf of Mexico
Nakai Nam Theun
Forest Area Laos
WildlifeQueen Elizabeth National
ParkUganda
No patrols
Higher
density
Lower densityx
Yang Ford Brown
59
Uncertainty in Adversary Decision Bounded Rationality
Human Subjects as Poachers
59
Uncertainty in Adversary Decision[2009]
Human subjects Anchoring e-Optimality
-4
-3
-2
-1
0
1
Unobserved 5 Observations 20 Observations Unlimited
Average expected reward
DOBSS
Uniform
COBRA
ARMOR
ARMOR Outperforms uniform random
COBRA
MqxCaq
Xxxts
qxR
ji
Xi
ijj
Xi Qj
jiijqx
)1()()1(
|)|1()1(
max
ee
Anchoring
e-optimality
With Sarit Kraus 44
Pita
5945
Quantal Response Stochastic choice better choice more likely
Quantal Response Model of Adversary [2011] Not Maximize Expected Utility
T
j
jxadversary
EU
jxadversary
EU
j
e
eq
1
))((
))((
-3
-25
-2
-15
-1
-05
0
05
1
15
2
Payoff 1 Payoff 2 Payoff 3 Payoff 4
QuantalResponse
COBRA
ARMOR
Adversaryrsquos probability
of choosing target j
Yang
10
)(max1
1
)(
)(
t tt
defendT
jT
j
jxEU
jxEU
x
xKxts
jxEU
e
e
adversary
adversary
59
Uncertainty in Adversary Decision [2012]
Robust vs Modeling Adversaries
Robustness Bound loss to defender Not model attacker via QR
Defeating Robust Learned subjective utility
Robust wins Draw QR wins
42 52 6
Results on 100 games
SU-QR wins Draw Robust
13 8 1
Results on 22 games
MqxCa
qxR
ji
Xi
ij
jiij
Xi Qj
qx
)1()(0
max
β (Adversaryrsquos utility loss if
deviates from optimal) gt=
(Defenderrsquos utility loss due to
adversary deviation)
penaltyattackw
rewardattackw
probcapturewjaSEU
3
2
1)(
M
j
jxSEU
jxSEU
jadversary
adversary
e
eq
1
)(
)(
SU-QR wins Draw Robust
6 13 3
Results against security experts
With Sarit Kraus 46
5947
PAWS Protection Assistant for Wildlife Security[2014]Repeated Stackelberg Game
Simulation
Learn from crime data
Improve model
Defender calculates
mixed strategy
Defender executes
randomized patrols
Poachers attack targets
119890119878119864119880119894(w1w2w3)
119896 119890119878119864119880119896(w1w2w3)
Bayesian SUQR Heterogeneous Poachers
5948
PAWS Test April 2014
Trials in Queen Elizabeth National Park
Andrew Lemieux with rangers
on PAWS patrol in Uganda
59
Security Resource OptimizationEvaluating Deployed Security Systems Not Easy
Game theory Improvement over previous approaches
Previous Human schedulers or ldquosimple randomrdquo
49
Lab
Evaluation
Simulated
adversary
Human subject
adversaries
Field Evaluation
Patrol quality
Unpredictable Cover
Compare real schedules
Scheduling competition
Expert evaluation
Field Evaluation
Tests against adversaries
ldquoMock attackersrdquo
Capture rates of
real adversaries
59
Why Does Game Theory Perform Better
Weaknesses of Previous Methods
Human schedulers
Predictable patterns eg US Coast Guard
Scheduling effort amp cognitive burden
Simple random (eg dice roll)
Wrong weightscoverage eg officers to sparsely crowded terminals
No adversary reactions
Multiple deployments over multiple years without us forcing them
50
59
Lab Evaluation via Simulations
Example from IRIS (FAMS)
-10
-8
-6
-4
-2
0
2
4
6
50 150 250
Defe
nde
r E
xpe
cte
d u
tilit
y
Schedule Size
Uniform Weighted random 1 Weighted random 2 IRIS
51
59
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage
Patrols Before PROTECT Boston Patrols After PROTECT Boston
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Base Patrol Area
52
PROTECT (Coast Guard) 350 increase defender expected utility
59
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage for Less Effort
53
IRIS for FAMS Outperformed expert human over six monthsReportGAO-09-903T
ARMOR at LAX Savings of up to an hour a day in scheduling
59
Field Evaluation Human vs Game Theory Competition
Counter-terrorism Patrol Scheduling
90 officers on LA Metro Trains
Humans required significant effort
Worse schedules than game theory
Observerrsquos report on questions
54
3
35
4
45
5
55
Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Q9
Q10
Q11
Q12
Secu
rity
Sco
re
Human Game Theory
59
Field Test Against Adversaries Mock Attackers
Example from PROTECT
ldquoMock attackerrdquo team deployed in Boston
Comparing PRE- to POST-PROTECT ldquodeterrencerdquo improved
Additional real-world indicators from Boston
Boston boaters questions
ldquohas the Coast Guard recently acquired more boatsrdquo
POST-PROTECT Actual reports of illegal activity
55
5956
Field Tests Against Adversaries
Computational Game Theory in the Field
Game theory vs Random
21 days of patrol
Identical conditions
Random + Human
Not controlled
0
20
40
60
80
100 Miscellaneous
Drugs
Firearm Violations
0
5
10
15
20
Captures30 min
Warnings30 min
Violations30 min
Game Theory
Rand+Human
Controlled
59
Expert Evaluation
Example from ARMOR IRIS amp PROTECT
February 2009 Commendations
LAX Police (City of Los Angeles)
July 2011 Operational Excellence
Award (US Coast Guard Boston)
September 2011 Certificate of
Appreciation (Federal Air Marshals)
June 2013 Meritorious Team Commendation
from Commandant (US Coast Guard)
57
59
Summary Security Games
Decision aids based on computational game theory in daily use
Optimize limited security resources against adversaries
Applications yield research challenges Science of security games
Scale-up Incremental strategy generation amp Marginals
Uncertainty Integrate MDPs Robustness Quantal response
Current applications (wildlife security) Interdisciplinar challenge
Global challenges Merge planninglearning amp security games
58
59
Just the Beginning of ldquoSecurity Gamesrdquohellip
Our next steps
59
Thank you
to sponsors
StartupARMORWAY Game theory in the field
Follow on research amp applications
Privacy audits
(Sinha IJCAIrsquo13)
Software testing
(Kukreja ASErsquo13)
Sport event security
(Yin AAAIrsquo14)
Singapore train
(Varakantham IAAIrsquo13)
Exam questions
(Li IJCAIrsquo13)
59
THANK YOU
tambeuscedu
httpteamcoreuscedusecurity
60
5911
bull 6 plots against LAX
ARMOR LAX (2007) GUARDS TSA (2011)
Airport Security Mapping to Stackelberg Games
GLASGOW 63007
5912
ARMOR Operation [2007]Generate Detailed Defender Schedule Pita Paruchuri
Mixed Integer Program
Pr(Canine patrol 8 AM Terminals 357) = 033
Pr(Canine patrol 8 AM Terminals 256) = 017
helliphellipCanine Team Schedule July 28Term 1 Term 2 Term 3 Term 4 Term 5 Term 6 Term 7 Term 8
8 AM Team1 Team3 Team5
9 AM Team1 Team2 Team4
10 AM Team3 Team5 Team2
Target 1 Target 2
Defender 1 2 -1 -3 4
Defender 2 -3 3 3 -2
5913
ARMOR MIP [2007]Generate Mixed Strategy for Defender
1 i
ixts
jq
ix
ijR
Xi Qj
max
1Qj
jq
MqxCa ji
Xi
ij )1()(0
Maximize defender
expected utility
Defender mixed
strategy
Adversary best
response
Pita Paruchuri
Target 1 Target 2
Defender 1 2 -1 -3 4
Defender 2 -3 3 3 -2
Adversary response
5914
ARMOR Payoffs [2007]Previous Research Provides Payoffs in Security Game Domains
Target 1 Target 2
Defender 1 2 -1 -3 4
Defender 2 -3 3 3 -2
jq
ix
ijR
Xi Qj
maxMaximize defender
expected utility
5915
ARMOR MIP [2007]Solving for a Single Adversary Type
Term 1 Term 2
Defend1 2 -1 -3 4
Defend2 -3 1 3 -3
1 i
ixts
jq
ix
ijR
Xi Qj
max
1Qj
jq
ljq][ix
MqxCa ji
Xi
ij
1010
)1()(0
Maximize defender
expected utility
Defender strategy
Adversary strategy
Adversary best
response
119909119894
ARMORhellipthrows a digital cloak of invisibilityhellip
59
IRIS Federal Air Marshals Service [2009]Scale Up Number of Defender Strategies
1000 Flights 20 air marshals 1041
combinations
ARMOR out of memory
Strategy 1 Strategy 2 Strategy 3
Strateg
y 1
Strateg
y 2
Strateg
y 3
Strateg
y 4
Strateg
y 5
Strateg
y 6
Not enumerate all combinations
Branch and price Incremental strategy generation
Strategy 1 Strategy 2 Strategy 3
Strategy 1
Strategy 2
Strategy 3
Strategy 4
Strategy 5
Strategy 6
16
59
IRIS Scale Up Number of Defender Strategies [2009]
Small Support Set for Mixed Strategies
Small support set size
bull Most xi variables zero
x124=0239
x123=00
x135=00
Attack
1
Attack
2
Attack
hellip
Attack
1000
123 5-10 4-8 hellip -209
124 5-10 4-8 hellip -209
135 5-10 -95 hellip -209
hellip
hellip1041 rowsx378=0123
10]10[
)1()(0
11
max
jqx
MqxCa
qxts
qxR
i
ji
Xi
ij
Qj
j
i
i
jiij
Xi Qj
qx
1000 flights 20 air marshals
1041combinations
17
59
Target 3 Target 7
hellip hellip
Resource Sink
Best new pure strategy
Minimum cost network flow
IRIS Incremental Strategy Generation
Exploit Small Support
Attack 1 Attack 2 Attack Attack 6
124 5-10 4-8 hellip -209
378 -8 10 -810 hellip -810
hellip
500 rows
NOT 1041
Attack 1 Attack 2 Attackhellip Attack 6
124 5-10 4-8 hellip -209
Slave (LP Duality Theory)
Master
Converge
GLOBAL
OPTIMAL
Attack 1 Attack 2 Attackhellip Attack 6
124 5-10 4-8 hellip -209
378 -8 10 -810 hellip -810
18
Jain Kiekintveld
59
IRIS Deployed FAMS (2009-)
ldquohellipin 2011 the Military Operations Research Society selected a University
of Southern California project with FAMS on randomizing flight
schedules for the prestigious Rist Awardhelliprdquo
-R S Bray (TSA)
Transportation Security Subcommittee
US House of Representatives 2012
19
Significant change in operations
59
Networks Mumbai Police Checkpoints[2013]
150 edges 2 Checkpoints
150-choose-2 strategies
With V Conitzer 20
Jain
59
Networks Mumbai Police Checkpoints[2013]
Incremental Strategy Generation
With V Conitzer 21
Jain
Double oracle Converge to a global optimal
Path 1 Path 2
Checkpoint
strategy 15 -5 -1 1
Path 1 Path 2
Checkpoint
strategy 15 -5 -1 1
Checkpoint
strategy 2-5 5 2 -1
Defender oracle
Path 1 Path 2 Path 3
Checkpoint
strategy 15 -5 -1 1 -2 2
Checkpoint
strategy 2-5 5 1 -1 -2 2
Attacker oracle
Path 1 Path 2 Path 3
Checkpoint
strategy 15 -5 -1 1 -2 2
Checkpoint
strategy 2-5 5 1 -1 -2 2
59
Double Oracle[2013]Incremental Strategy Generation Exploit Small Support
22
150 edges 2 Checkpoints
Only six candidate edges
for checkpoints
Jain
5923
Mumbai Police Checkpoints[2013]Results of Scale-up
20416 Roads15 checkpoints
20 min
59
Double Oracle Social Cyber Networks[2013]
Incremental Strategy Generation
24
Social networkseg counter-insurgency
Sources
Intermediate
Nodes
Links
Targets
Cyber networks
Tsai
59
Outline ldquoSecurity Gamesrdquo Research
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
25
59
Port Security Threat Scenarios
US Ports $315 trillion economy
USS Cole after suicide attack
French oil tanker hit by small boat
Attack on a ferry
26
59
PROTECT Randomized Patrol Scheduling [2013]
27
Port Protection (Scale-up) and Ferries (Continuous Spacetime)
59
PROTECT Randomized Patrol Scheduling [2013]Port Protection (Scale-up) and Ferries (Continuous Spacetime)
28
59
Ferries Scale-up with Mobile Resources amp Moving TargetsTransition Graph Representation
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
29
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Ferries Scale-up with Mobile Resources amp Moving TargetsTransition Graph Representation
30
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
31
Patrols protect nearby ferry location Solve as done in ARMOR
Ferries Patrol RoutesExponential Numbers of Patrol Routes
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Patrols protect nearby ferry location Solve as done in ARMOR
Ferries Patrol RoutesExponential Numbers of Patrol Routes
32
Pr([(B5) (C 10) (C15)]) = 017
Pr([(B5) (C10) (B15)]) =013
NT variables
Pr([(A5) (A10) (B15)]) = 007
Pr([(A5) (A10) (A15)]) = 003
Jiang KiekintveldFang
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Variables NOT routes but probability flow over each segment
Ferries Scale-up Marginal Probabilities Over Segments
33
NT variables
Jiang KiekintveldFang
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
030
017
013
Obtain marginal probabilities over segments
010
007
003
N2T variablesExtract Pr([(B5) (C 10) (C15)]) = 017
Pr([(B5) (C10) (B15)]) =013
Ferries Scale-up with Marginals Over Separable SegmentsSignificant Speedup
34
NT variables
59
5936
Outline ldquoSecurity Gamesrdquo Research (2007-Now)
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
Evaluation II Real-world deployments
(Patience)
Evaluation I Scale up Handle uncertainty
59
TRUSTS Frequent adversary interaction games
Patrols Against Fare Evaders
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
017
013
010
010
37
59
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
38
Jiang Delle Fave
59
Markov Decision Problems in Security games
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
39
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution Jiang Delle Fave
5940
Urban Transportation Security
MDPs amp DEC-MDPs in Security Games
COPS LA Metro System(Against Opportunistic Crime)
STREETS Singapore Roads(Against Reckless Driving)
DEC-MDPs in Security Games Teamwork + Uncertainty
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
ZhangShieh
59
Uncertainty Space Algorithms
Bayesian and Robust Approaches
Adversary payoff uncertainty
Adversary observation amp
defender execution
uncertainty
Adversary rationality
uncertainty
RECON
BRASS
Monotonic Maximin
(Monotonic adversary)
41
URAC
Payoff interval
Not point
estimateGMC HUNTER
Bayesian
Robust
Yin Nguyen An
0
05
1
15
5 10
Def
end
ers
EU
Targets
ISG RECON URAC
59
Security Games Environmental Crime
amp Bounded Rationality
FisheryGulf of Mexico
Nakai Nam Theun
Forest Area Laos
WildlifeQueen Elizabeth National
ParkUganda
No patrols
Higher
density
Lower densityx
Yang Ford Brown
59
Uncertainty in Adversary Decision Bounded Rationality
Human Subjects as Poachers
59
Uncertainty in Adversary Decision[2009]
Human subjects Anchoring e-Optimality
-4
-3
-2
-1
0
1
Unobserved 5 Observations 20 Observations Unlimited
Average expected reward
DOBSS
Uniform
COBRA
ARMOR
ARMOR Outperforms uniform random
COBRA
MqxCaq
Xxxts
qxR
ji
Xi
ijj
Xi Qj
jiijqx
)1()()1(
|)|1()1(
max
ee
Anchoring
e-optimality
With Sarit Kraus 44
Pita
5945
Quantal Response Stochastic choice better choice more likely
Quantal Response Model of Adversary [2011] Not Maximize Expected Utility
T
j
jxadversary
EU
jxadversary
EU
j
e
eq
1
))((
))((
-3
-25
-2
-15
-1
-05
0
05
1
15
2
Payoff 1 Payoff 2 Payoff 3 Payoff 4
QuantalResponse
COBRA
ARMOR
Adversaryrsquos probability
of choosing target j
Yang
10
)(max1
1
)(
)(
t tt
defendT
jT
j
jxEU
jxEU
x
xKxts
jxEU
e
e
adversary
adversary
59
Uncertainty in Adversary Decision [2012]
Robust vs Modeling Adversaries
Robustness Bound loss to defender Not model attacker via QR
Defeating Robust Learned subjective utility
Robust wins Draw QR wins
42 52 6
Results on 100 games
SU-QR wins Draw Robust
13 8 1
Results on 22 games
MqxCa
qxR
ji
Xi
ij
jiij
Xi Qj
qx
)1()(0
max
β (Adversaryrsquos utility loss if
deviates from optimal) gt=
(Defenderrsquos utility loss due to
adversary deviation)
penaltyattackw
rewardattackw
probcapturewjaSEU
3
2
1)(
M
j
jxSEU
jxSEU
jadversary
adversary
e
eq
1
)(
)(
SU-QR wins Draw Robust
6 13 3
Results against security experts
With Sarit Kraus 46
5947
PAWS Protection Assistant for Wildlife Security[2014]Repeated Stackelberg Game
Simulation
Learn from crime data
Improve model
Defender calculates
mixed strategy
Defender executes
randomized patrols
Poachers attack targets
119890119878119864119880119894(w1w2w3)
119896 119890119878119864119880119896(w1w2w3)
Bayesian SUQR Heterogeneous Poachers
5948
PAWS Test April 2014
Trials in Queen Elizabeth National Park
Andrew Lemieux with rangers
on PAWS patrol in Uganda
59
Security Resource OptimizationEvaluating Deployed Security Systems Not Easy
Game theory Improvement over previous approaches
Previous Human schedulers or ldquosimple randomrdquo
49
Lab
Evaluation
Simulated
adversary
Human subject
adversaries
Field Evaluation
Patrol quality
Unpredictable Cover
Compare real schedules
Scheduling competition
Expert evaluation
Field Evaluation
Tests against adversaries
ldquoMock attackersrdquo
Capture rates of
real adversaries
59
Why Does Game Theory Perform Better
Weaknesses of Previous Methods
Human schedulers
Predictable patterns eg US Coast Guard
Scheduling effort amp cognitive burden
Simple random (eg dice roll)
Wrong weightscoverage eg officers to sparsely crowded terminals
No adversary reactions
Multiple deployments over multiple years without us forcing them
50
59
Lab Evaluation via Simulations
Example from IRIS (FAMS)
-10
-8
-6
-4
-2
0
2
4
6
50 150 250
Defe
nde
r E
xpe
cte
d u
tilit
y
Schedule Size
Uniform Weighted random 1 Weighted random 2 IRIS
51
59
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage
Patrols Before PROTECT Boston Patrols After PROTECT Boston
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Base Patrol Area
52
PROTECT (Coast Guard) 350 increase defender expected utility
59
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage for Less Effort
53
IRIS for FAMS Outperformed expert human over six monthsReportGAO-09-903T
ARMOR at LAX Savings of up to an hour a day in scheduling
59
Field Evaluation Human vs Game Theory Competition
Counter-terrorism Patrol Scheduling
90 officers on LA Metro Trains
Humans required significant effort
Worse schedules than game theory
Observerrsquos report on questions
54
3
35
4
45
5
55
Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Q9
Q10
Q11
Q12
Secu
rity
Sco
re
Human Game Theory
59
Field Test Against Adversaries Mock Attackers
Example from PROTECT
ldquoMock attackerrdquo team deployed in Boston
Comparing PRE- to POST-PROTECT ldquodeterrencerdquo improved
Additional real-world indicators from Boston
Boston boaters questions
ldquohas the Coast Guard recently acquired more boatsrdquo
POST-PROTECT Actual reports of illegal activity
55
5956
Field Tests Against Adversaries
Computational Game Theory in the Field
Game theory vs Random
21 days of patrol
Identical conditions
Random + Human
Not controlled
0
20
40
60
80
100 Miscellaneous
Drugs
Firearm Violations
0
5
10
15
20
Captures30 min
Warnings30 min
Violations30 min
Game Theory
Rand+Human
Controlled
59
Expert Evaluation
Example from ARMOR IRIS amp PROTECT
February 2009 Commendations
LAX Police (City of Los Angeles)
July 2011 Operational Excellence
Award (US Coast Guard Boston)
September 2011 Certificate of
Appreciation (Federal Air Marshals)
June 2013 Meritorious Team Commendation
from Commandant (US Coast Guard)
57
59
Summary Security Games
Decision aids based on computational game theory in daily use
Optimize limited security resources against adversaries
Applications yield research challenges Science of security games
Scale-up Incremental strategy generation amp Marginals
Uncertainty Integrate MDPs Robustness Quantal response
Current applications (wildlife security) Interdisciplinar challenge
Global challenges Merge planninglearning amp security games
58
59
Just the Beginning of ldquoSecurity Gamesrdquohellip
Our next steps
59
Thank you
to sponsors
StartupARMORWAY Game theory in the field
Follow on research amp applications
Privacy audits
(Sinha IJCAIrsquo13)
Software testing
(Kukreja ASErsquo13)
Sport event security
(Yin AAAIrsquo14)
Singapore train
(Varakantham IAAIrsquo13)
Exam questions
(Li IJCAIrsquo13)
59
THANK YOU
tambeuscedu
httpteamcoreuscedusecurity
60
5912
ARMOR Operation [2007]Generate Detailed Defender Schedule Pita Paruchuri
Mixed Integer Program
Pr(Canine patrol 8 AM Terminals 357) = 033
Pr(Canine patrol 8 AM Terminals 256) = 017
helliphellipCanine Team Schedule July 28Term 1 Term 2 Term 3 Term 4 Term 5 Term 6 Term 7 Term 8
8 AM Team1 Team3 Team5
9 AM Team1 Team2 Team4
10 AM Team3 Team5 Team2
Target 1 Target 2
Defender 1 2 -1 -3 4
Defender 2 -3 3 3 -2
5913
ARMOR MIP [2007]Generate Mixed Strategy for Defender
1 i
ixts
jq
ix
ijR
Xi Qj
max
1Qj
jq
MqxCa ji
Xi
ij )1()(0
Maximize defender
expected utility
Defender mixed
strategy
Adversary best
response
Pita Paruchuri
Target 1 Target 2
Defender 1 2 -1 -3 4
Defender 2 -3 3 3 -2
Adversary response
5914
ARMOR Payoffs [2007]Previous Research Provides Payoffs in Security Game Domains
Target 1 Target 2
Defender 1 2 -1 -3 4
Defender 2 -3 3 3 -2
jq
ix
ijR
Xi Qj
maxMaximize defender
expected utility
5915
ARMOR MIP [2007]Solving for a Single Adversary Type
Term 1 Term 2
Defend1 2 -1 -3 4
Defend2 -3 1 3 -3
1 i
ixts
jq
ix
ijR
Xi Qj
max
1Qj
jq
ljq][ix
MqxCa ji
Xi
ij
1010
)1()(0
Maximize defender
expected utility
Defender strategy
Adversary strategy
Adversary best
response
119909119894
ARMORhellipthrows a digital cloak of invisibilityhellip
59
IRIS Federal Air Marshals Service [2009]Scale Up Number of Defender Strategies
1000 Flights 20 air marshals 1041
combinations
ARMOR out of memory
Strategy 1 Strategy 2 Strategy 3
Strateg
y 1
Strateg
y 2
Strateg
y 3
Strateg
y 4
Strateg
y 5
Strateg
y 6
Not enumerate all combinations
Branch and price Incremental strategy generation
Strategy 1 Strategy 2 Strategy 3
Strategy 1
Strategy 2
Strategy 3
Strategy 4
Strategy 5
Strategy 6
16
59
IRIS Scale Up Number of Defender Strategies [2009]
Small Support Set for Mixed Strategies
Small support set size
bull Most xi variables zero
x124=0239
x123=00
x135=00
Attack
1
Attack
2
Attack
hellip
Attack
1000
123 5-10 4-8 hellip -209
124 5-10 4-8 hellip -209
135 5-10 -95 hellip -209
hellip
hellip1041 rowsx378=0123
10]10[
)1()(0
11
max
jqx
MqxCa
qxts
qxR
i
ji
Xi
ij
Qj
j
i
i
jiij
Xi Qj
qx
1000 flights 20 air marshals
1041combinations
17
59
Target 3 Target 7
hellip hellip
Resource Sink
Best new pure strategy
Minimum cost network flow
IRIS Incremental Strategy Generation
Exploit Small Support
Attack 1 Attack 2 Attack Attack 6
124 5-10 4-8 hellip -209
378 -8 10 -810 hellip -810
hellip
500 rows
NOT 1041
Attack 1 Attack 2 Attackhellip Attack 6
124 5-10 4-8 hellip -209
Slave (LP Duality Theory)
Master
Converge
GLOBAL
OPTIMAL
Attack 1 Attack 2 Attackhellip Attack 6
124 5-10 4-8 hellip -209
378 -8 10 -810 hellip -810
18
Jain Kiekintveld
59
IRIS Deployed FAMS (2009-)
ldquohellipin 2011 the Military Operations Research Society selected a University
of Southern California project with FAMS on randomizing flight
schedules for the prestigious Rist Awardhelliprdquo
-R S Bray (TSA)
Transportation Security Subcommittee
US House of Representatives 2012
19
Significant change in operations
59
Networks Mumbai Police Checkpoints[2013]
150 edges 2 Checkpoints
150-choose-2 strategies
With V Conitzer 20
Jain
59
Networks Mumbai Police Checkpoints[2013]
Incremental Strategy Generation
With V Conitzer 21
Jain
Double oracle Converge to a global optimal
Path 1 Path 2
Checkpoint
strategy 15 -5 -1 1
Path 1 Path 2
Checkpoint
strategy 15 -5 -1 1
Checkpoint
strategy 2-5 5 2 -1
Defender oracle
Path 1 Path 2 Path 3
Checkpoint
strategy 15 -5 -1 1 -2 2
Checkpoint
strategy 2-5 5 1 -1 -2 2
Attacker oracle
Path 1 Path 2 Path 3
Checkpoint
strategy 15 -5 -1 1 -2 2
Checkpoint
strategy 2-5 5 1 -1 -2 2
59
Double Oracle[2013]Incremental Strategy Generation Exploit Small Support
22
150 edges 2 Checkpoints
Only six candidate edges
for checkpoints
Jain
5923
Mumbai Police Checkpoints[2013]Results of Scale-up
20416 Roads15 checkpoints
20 min
59
Double Oracle Social Cyber Networks[2013]
Incremental Strategy Generation
24
Social networkseg counter-insurgency
Sources
Intermediate
Nodes
Links
Targets
Cyber networks
Tsai
59
Outline ldquoSecurity Gamesrdquo Research
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
25
59
Port Security Threat Scenarios
US Ports $315 trillion economy
USS Cole after suicide attack
French oil tanker hit by small boat
Attack on a ferry
26
59
PROTECT Randomized Patrol Scheduling [2013]
27
Port Protection (Scale-up) and Ferries (Continuous Spacetime)
59
PROTECT Randomized Patrol Scheduling [2013]Port Protection (Scale-up) and Ferries (Continuous Spacetime)
28
59
Ferries Scale-up with Mobile Resources amp Moving TargetsTransition Graph Representation
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
29
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Ferries Scale-up with Mobile Resources amp Moving TargetsTransition Graph Representation
30
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
31
Patrols protect nearby ferry location Solve as done in ARMOR
Ferries Patrol RoutesExponential Numbers of Patrol Routes
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Patrols protect nearby ferry location Solve as done in ARMOR
Ferries Patrol RoutesExponential Numbers of Patrol Routes
32
Pr([(B5) (C 10) (C15)]) = 017
Pr([(B5) (C10) (B15)]) =013
NT variables
Pr([(A5) (A10) (B15)]) = 007
Pr([(A5) (A10) (A15)]) = 003
Jiang KiekintveldFang
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Variables NOT routes but probability flow over each segment
Ferries Scale-up Marginal Probabilities Over Segments
33
NT variables
Jiang KiekintveldFang
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
030
017
013
Obtain marginal probabilities over segments
010
007
003
N2T variablesExtract Pr([(B5) (C 10) (C15)]) = 017
Pr([(B5) (C10) (B15)]) =013
Ferries Scale-up with Marginals Over Separable SegmentsSignificant Speedup
34
NT variables
59
5936
Outline ldquoSecurity Gamesrdquo Research (2007-Now)
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
Evaluation II Real-world deployments
(Patience)
Evaluation I Scale up Handle uncertainty
59
TRUSTS Frequent adversary interaction games
Patrols Against Fare Evaders
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
017
013
010
010
37
59
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
38
Jiang Delle Fave
59
Markov Decision Problems in Security games
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
39
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution Jiang Delle Fave
5940
Urban Transportation Security
MDPs amp DEC-MDPs in Security Games
COPS LA Metro System(Against Opportunistic Crime)
STREETS Singapore Roads(Against Reckless Driving)
DEC-MDPs in Security Games Teamwork + Uncertainty
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
ZhangShieh
59
Uncertainty Space Algorithms
Bayesian and Robust Approaches
Adversary payoff uncertainty
Adversary observation amp
defender execution
uncertainty
Adversary rationality
uncertainty
RECON
BRASS
Monotonic Maximin
(Monotonic adversary)
41
URAC
Payoff interval
Not point
estimateGMC HUNTER
Bayesian
Robust
Yin Nguyen An
0
05
1
15
5 10
Def
end
ers
EU
Targets
ISG RECON URAC
59
Security Games Environmental Crime
amp Bounded Rationality
FisheryGulf of Mexico
Nakai Nam Theun
Forest Area Laos
WildlifeQueen Elizabeth National
ParkUganda
No patrols
Higher
density
Lower densityx
Yang Ford Brown
59
Uncertainty in Adversary Decision Bounded Rationality
Human Subjects as Poachers
59
Uncertainty in Adversary Decision[2009]
Human subjects Anchoring e-Optimality
-4
-3
-2
-1
0
1
Unobserved 5 Observations 20 Observations Unlimited
Average expected reward
DOBSS
Uniform
COBRA
ARMOR
ARMOR Outperforms uniform random
COBRA
MqxCaq
Xxxts
qxR
ji
Xi
ijj
Xi Qj
jiijqx
)1()()1(
|)|1()1(
max
ee
Anchoring
e-optimality
With Sarit Kraus 44
Pita
5945
Quantal Response Stochastic choice better choice more likely
Quantal Response Model of Adversary [2011] Not Maximize Expected Utility
T
j
jxadversary
EU
jxadversary
EU
j
e
eq
1
))((
))((
-3
-25
-2
-15
-1
-05
0
05
1
15
2
Payoff 1 Payoff 2 Payoff 3 Payoff 4
QuantalResponse
COBRA
ARMOR
Adversaryrsquos probability
of choosing target j
Yang
10
)(max1
1
)(
)(
t tt
defendT
jT
j
jxEU
jxEU
x
xKxts
jxEU
e
e
adversary
adversary
59
Uncertainty in Adversary Decision [2012]
Robust vs Modeling Adversaries
Robustness Bound loss to defender Not model attacker via QR
Defeating Robust Learned subjective utility
Robust wins Draw QR wins
42 52 6
Results on 100 games
SU-QR wins Draw Robust
13 8 1
Results on 22 games
MqxCa
qxR
ji
Xi
ij
jiij
Xi Qj
qx
)1()(0
max
β (Adversaryrsquos utility loss if
deviates from optimal) gt=
(Defenderrsquos utility loss due to
adversary deviation)
penaltyattackw
rewardattackw
probcapturewjaSEU
3
2
1)(
M
j
jxSEU
jxSEU
jadversary
adversary
e
eq
1
)(
)(
SU-QR wins Draw Robust
6 13 3
Results against security experts
With Sarit Kraus 46
5947
PAWS Protection Assistant for Wildlife Security[2014]Repeated Stackelberg Game
Simulation
Learn from crime data
Improve model
Defender calculates
mixed strategy
Defender executes
randomized patrols
Poachers attack targets
119890119878119864119880119894(w1w2w3)
119896 119890119878119864119880119896(w1w2w3)
Bayesian SUQR Heterogeneous Poachers
5948
PAWS Test April 2014
Trials in Queen Elizabeth National Park
Andrew Lemieux with rangers
on PAWS patrol in Uganda
59
Security Resource OptimizationEvaluating Deployed Security Systems Not Easy
Game theory Improvement over previous approaches
Previous Human schedulers or ldquosimple randomrdquo
49
Lab
Evaluation
Simulated
adversary
Human subject
adversaries
Field Evaluation
Patrol quality
Unpredictable Cover
Compare real schedules
Scheduling competition
Expert evaluation
Field Evaluation
Tests against adversaries
ldquoMock attackersrdquo
Capture rates of
real adversaries
59
Why Does Game Theory Perform Better
Weaknesses of Previous Methods
Human schedulers
Predictable patterns eg US Coast Guard
Scheduling effort amp cognitive burden
Simple random (eg dice roll)
Wrong weightscoverage eg officers to sparsely crowded terminals
No adversary reactions
Multiple deployments over multiple years without us forcing them
50
59
Lab Evaluation via Simulations
Example from IRIS (FAMS)
-10
-8
-6
-4
-2
0
2
4
6
50 150 250
Defe
nde
r E
xpe
cte
d u
tilit
y
Schedule Size
Uniform Weighted random 1 Weighted random 2 IRIS
51
59
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage
Patrols Before PROTECT Boston Patrols After PROTECT Boston
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Base Patrol Area
52
PROTECT (Coast Guard) 350 increase defender expected utility
59
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage for Less Effort
53
IRIS for FAMS Outperformed expert human over six monthsReportGAO-09-903T
ARMOR at LAX Savings of up to an hour a day in scheduling
59
Field Evaluation Human vs Game Theory Competition
Counter-terrorism Patrol Scheduling
90 officers on LA Metro Trains
Humans required significant effort
Worse schedules than game theory
Observerrsquos report on questions
54
3
35
4
45
5
55
Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Q9
Q10
Q11
Q12
Secu
rity
Sco
re
Human Game Theory
59
Field Test Against Adversaries Mock Attackers
Example from PROTECT
ldquoMock attackerrdquo team deployed in Boston
Comparing PRE- to POST-PROTECT ldquodeterrencerdquo improved
Additional real-world indicators from Boston
Boston boaters questions
ldquohas the Coast Guard recently acquired more boatsrdquo
POST-PROTECT Actual reports of illegal activity
55
5956
Field Tests Against Adversaries
Computational Game Theory in the Field
Game theory vs Random
21 days of patrol
Identical conditions
Random + Human
Not controlled
0
20
40
60
80
100 Miscellaneous
Drugs
Firearm Violations
0
5
10
15
20
Captures30 min
Warnings30 min
Violations30 min
Game Theory
Rand+Human
Controlled
59
Expert Evaluation
Example from ARMOR IRIS amp PROTECT
February 2009 Commendations
LAX Police (City of Los Angeles)
July 2011 Operational Excellence
Award (US Coast Guard Boston)
September 2011 Certificate of
Appreciation (Federal Air Marshals)
June 2013 Meritorious Team Commendation
from Commandant (US Coast Guard)
57
59
Summary Security Games
Decision aids based on computational game theory in daily use
Optimize limited security resources against adversaries
Applications yield research challenges Science of security games
Scale-up Incremental strategy generation amp Marginals
Uncertainty Integrate MDPs Robustness Quantal response
Current applications (wildlife security) Interdisciplinar challenge
Global challenges Merge planninglearning amp security games
58
59
Just the Beginning of ldquoSecurity Gamesrdquohellip
Our next steps
59
Thank you
to sponsors
StartupARMORWAY Game theory in the field
Follow on research amp applications
Privacy audits
(Sinha IJCAIrsquo13)
Software testing
(Kukreja ASErsquo13)
Sport event security
(Yin AAAIrsquo14)
Singapore train
(Varakantham IAAIrsquo13)
Exam questions
(Li IJCAIrsquo13)
59
THANK YOU
tambeuscedu
httpteamcoreuscedusecurity
60
5913
ARMOR MIP [2007]Generate Mixed Strategy for Defender
1 i
ixts
jq
ix
ijR
Xi Qj
max
1Qj
jq
MqxCa ji
Xi
ij )1()(0
Maximize defender
expected utility
Defender mixed
strategy
Adversary best
response
Pita Paruchuri
Target 1 Target 2
Defender 1 2 -1 -3 4
Defender 2 -3 3 3 -2
Adversary response
5914
ARMOR Payoffs [2007]Previous Research Provides Payoffs in Security Game Domains
Target 1 Target 2
Defender 1 2 -1 -3 4
Defender 2 -3 3 3 -2
jq
ix
ijR
Xi Qj
maxMaximize defender
expected utility
5915
ARMOR MIP [2007]Solving for a Single Adversary Type
Term 1 Term 2
Defend1 2 -1 -3 4
Defend2 -3 1 3 -3
1 i
ixts
jq
ix
ijR
Xi Qj
max
1Qj
jq
ljq][ix
MqxCa ji
Xi
ij
1010
)1()(0
Maximize defender
expected utility
Defender strategy
Adversary strategy
Adversary best
response
119909119894
ARMORhellipthrows a digital cloak of invisibilityhellip
59
IRIS Federal Air Marshals Service [2009]Scale Up Number of Defender Strategies
1000 Flights 20 air marshals 1041
combinations
ARMOR out of memory
Strategy 1 Strategy 2 Strategy 3
Strateg
y 1
Strateg
y 2
Strateg
y 3
Strateg
y 4
Strateg
y 5
Strateg
y 6
Not enumerate all combinations
Branch and price Incremental strategy generation
Strategy 1 Strategy 2 Strategy 3
Strategy 1
Strategy 2
Strategy 3
Strategy 4
Strategy 5
Strategy 6
16
59
IRIS Scale Up Number of Defender Strategies [2009]
Small Support Set for Mixed Strategies
Small support set size
bull Most xi variables zero
x124=0239
x123=00
x135=00
Attack
1
Attack
2
Attack
hellip
Attack
1000
123 5-10 4-8 hellip -209
124 5-10 4-8 hellip -209
135 5-10 -95 hellip -209
hellip
hellip1041 rowsx378=0123
10]10[
)1()(0
11
max
jqx
MqxCa
qxts
qxR
i
ji
Xi
ij
Qj
j
i
i
jiij
Xi Qj
qx
1000 flights 20 air marshals
1041combinations
17
59
Target 3 Target 7
hellip hellip
Resource Sink
Best new pure strategy
Minimum cost network flow
IRIS Incremental Strategy Generation
Exploit Small Support
Attack 1 Attack 2 Attack Attack 6
124 5-10 4-8 hellip -209
378 -8 10 -810 hellip -810
hellip
500 rows
NOT 1041
Attack 1 Attack 2 Attackhellip Attack 6
124 5-10 4-8 hellip -209
Slave (LP Duality Theory)
Master
Converge
GLOBAL
OPTIMAL
Attack 1 Attack 2 Attackhellip Attack 6
124 5-10 4-8 hellip -209
378 -8 10 -810 hellip -810
18
Jain Kiekintveld
59
IRIS Deployed FAMS (2009-)
ldquohellipin 2011 the Military Operations Research Society selected a University
of Southern California project with FAMS on randomizing flight
schedules for the prestigious Rist Awardhelliprdquo
-R S Bray (TSA)
Transportation Security Subcommittee
US House of Representatives 2012
19
Significant change in operations
59
Networks Mumbai Police Checkpoints[2013]
150 edges 2 Checkpoints
150-choose-2 strategies
With V Conitzer 20
Jain
59
Networks Mumbai Police Checkpoints[2013]
Incremental Strategy Generation
With V Conitzer 21
Jain
Double oracle Converge to a global optimal
Path 1 Path 2
Checkpoint
strategy 15 -5 -1 1
Path 1 Path 2
Checkpoint
strategy 15 -5 -1 1
Checkpoint
strategy 2-5 5 2 -1
Defender oracle
Path 1 Path 2 Path 3
Checkpoint
strategy 15 -5 -1 1 -2 2
Checkpoint
strategy 2-5 5 1 -1 -2 2
Attacker oracle
Path 1 Path 2 Path 3
Checkpoint
strategy 15 -5 -1 1 -2 2
Checkpoint
strategy 2-5 5 1 -1 -2 2
59
Double Oracle[2013]Incremental Strategy Generation Exploit Small Support
22
150 edges 2 Checkpoints
Only six candidate edges
for checkpoints
Jain
5923
Mumbai Police Checkpoints[2013]Results of Scale-up
20416 Roads15 checkpoints
20 min
59
Double Oracle Social Cyber Networks[2013]
Incremental Strategy Generation
24
Social networkseg counter-insurgency
Sources
Intermediate
Nodes
Links
Targets
Cyber networks
Tsai
59
Outline ldquoSecurity Gamesrdquo Research
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
25
59
Port Security Threat Scenarios
US Ports $315 trillion economy
USS Cole after suicide attack
French oil tanker hit by small boat
Attack on a ferry
26
59
PROTECT Randomized Patrol Scheduling [2013]
27
Port Protection (Scale-up) and Ferries (Continuous Spacetime)
59
PROTECT Randomized Patrol Scheduling [2013]Port Protection (Scale-up) and Ferries (Continuous Spacetime)
28
59
Ferries Scale-up with Mobile Resources amp Moving TargetsTransition Graph Representation
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
29
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Ferries Scale-up with Mobile Resources amp Moving TargetsTransition Graph Representation
30
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
31
Patrols protect nearby ferry location Solve as done in ARMOR
Ferries Patrol RoutesExponential Numbers of Patrol Routes
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Patrols protect nearby ferry location Solve as done in ARMOR
Ferries Patrol RoutesExponential Numbers of Patrol Routes
32
Pr([(B5) (C 10) (C15)]) = 017
Pr([(B5) (C10) (B15)]) =013
NT variables
Pr([(A5) (A10) (B15)]) = 007
Pr([(A5) (A10) (A15)]) = 003
Jiang KiekintveldFang
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Variables NOT routes but probability flow over each segment
Ferries Scale-up Marginal Probabilities Over Segments
33
NT variables
Jiang KiekintveldFang
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
030
017
013
Obtain marginal probabilities over segments
010
007
003
N2T variablesExtract Pr([(B5) (C 10) (C15)]) = 017
Pr([(B5) (C10) (B15)]) =013
Ferries Scale-up with Marginals Over Separable SegmentsSignificant Speedup
34
NT variables
59
5936
Outline ldquoSecurity Gamesrdquo Research (2007-Now)
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
Evaluation II Real-world deployments
(Patience)
Evaluation I Scale up Handle uncertainty
59
TRUSTS Frequent adversary interaction games
Patrols Against Fare Evaders
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
017
013
010
010
37
59
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
38
Jiang Delle Fave
59
Markov Decision Problems in Security games
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
39
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution Jiang Delle Fave
5940
Urban Transportation Security
MDPs amp DEC-MDPs in Security Games
COPS LA Metro System(Against Opportunistic Crime)
STREETS Singapore Roads(Against Reckless Driving)
DEC-MDPs in Security Games Teamwork + Uncertainty
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
ZhangShieh
59
Uncertainty Space Algorithms
Bayesian and Robust Approaches
Adversary payoff uncertainty
Adversary observation amp
defender execution
uncertainty
Adversary rationality
uncertainty
RECON
BRASS
Monotonic Maximin
(Monotonic adversary)
41
URAC
Payoff interval
Not point
estimateGMC HUNTER
Bayesian
Robust
Yin Nguyen An
0
05
1
15
5 10
Def
end
ers
EU
Targets
ISG RECON URAC
59
Security Games Environmental Crime
amp Bounded Rationality
FisheryGulf of Mexico
Nakai Nam Theun
Forest Area Laos
WildlifeQueen Elizabeth National
ParkUganda
No patrols
Higher
density
Lower densityx
Yang Ford Brown
59
Uncertainty in Adversary Decision Bounded Rationality
Human Subjects as Poachers
59
Uncertainty in Adversary Decision[2009]
Human subjects Anchoring e-Optimality
-4
-3
-2
-1
0
1
Unobserved 5 Observations 20 Observations Unlimited
Average expected reward
DOBSS
Uniform
COBRA
ARMOR
ARMOR Outperforms uniform random
COBRA
MqxCaq
Xxxts
qxR
ji
Xi
ijj
Xi Qj
jiijqx
)1()()1(
|)|1()1(
max
ee
Anchoring
e-optimality
With Sarit Kraus 44
Pita
5945
Quantal Response Stochastic choice better choice more likely
Quantal Response Model of Adversary [2011] Not Maximize Expected Utility
T
j
jxadversary
EU
jxadversary
EU
j
e
eq
1
))((
))((
-3
-25
-2
-15
-1
-05
0
05
1
15
2
Payoff 1 Payoff 2 Payoff 3 Payoff 4
QuantalResponse
COBRA
ARMOR
Adversaryrsquos probability
of choosing target j
Yang
10
)(max1
1
)(
)(
t tt
defendT
jT
j
jxEU
jxEU
x
xKxts
jxEU
e
e
adversary
adversary
59
Uncertainty in Adversary Decision [2012]
Robust vs Modeling Adversaries
Robustness Bound loss to defender Not model attacker via QR
Defeating Robust Learned subjective utility
Robust wins Draw QR wins
42 52 6
Results on 100 games
SU-QR wins Draw Robust
13 8 1
Results on 22 games
MqxCa
qxR
ji
Xi
ij
jiij
Xi Qj
qx
)1()(0
max
β (Adversaryrsquos utility loss if
deviates from optimal) gt=
(Defenderrsquos utility loss due to
adversary deviation)
penaltyattackw
rewardattackw
probcapturewjaSEU
3
2
1)(
M
j
jxSEU
jxSEU
jadversary
adversary
e
eq
1
)(
)(
SU-QR wins Draw Robust
6 13 3
Results against security experts
With Sarit Kraus 46
5947
PAWS Protection Assistant for Wildlife Security[2014]Repeated Stackelberg Game
Simulation
Learn from crime data
Improve model
Defender calculates
mixed strategy
Defender executes
randomized patrols
Poachers attack targets
119890119878119864119880119894(w1w2w3)
119896 119890119878119864119880119896(w1w2w3)
Bayesian SUQR Heterogeneous Poachers
5948
PAWS Test April 2014
Trials in Queen Elizabeth National Park
Andrew Lemieux with rangers
on PAWS patrol in Uganda
59
Security Resource OptimizationEvaluating Deployed Security Systems Not Easy
Game theory Improvement over previous approaches
Previous Human schedulers or ldquosimple randomrdquo
49
Lab
Evaluation
Simulated
adversary
Human subject
adversaries
Field Evaluation
Patrol quality
Unpredictable Cover
Compare real schedules
Scheduling competition
Expert evaluation
Field Evaluation
Tests against adversaries
ldquoMock attackersrdquo
Capture rates of
real adversaries
59
Why Does Game Theory Perform Better
Weaknesses of Previous Methods
Human schedulers
Predictable patterns eg US Coast Guard
Scheduling effort amp cognitive burden
Simple random (eg dice roll)
Wrong weightscoverage eg officers to sparsely crowded terminals
No adversary reactions
Multiple deployments over multiple years without us forcing them
50
59
Lab Evaluation via Simulations
Example from IRIS (FAMS)
-10
-8
-6
-4
-2
0
2
4
6
50 150 250
Defe
nde
r E
xpe
cte
d u
tilit
y
Schedule Size
Uniform Weighted random 1 Weighted random 2 IRIS
51
59
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage
Patrols Before PROTECT Boston Patrols After PROTECT Boston
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Base Patrol Area
52
PROTECT (Coast Guard) 350 increase defender expected utility
59
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage for Less Effort
53
IRIS for FAMS Outperformed expert human over six monthsReportGAO-09-903T
ARMOR at LAX Savings of up to an hour a day in scheduling
59
Field Evaluation Human vs Game Theory Competition
Counter-terrorism Patrol Scheduling
90 officers on LA Metro Trains
Humans required significant effort
Worse schedules than game theory
Observerrsquos report on questions
54
3
35
4
45
5
55
Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Q9
Q10
Q11
Q12
Secu
rity
Sco
re
Human Game Theory
59
Field Test Against Adversaries Mock Attackers
Example from PROTECT
ldquoMock attackerrdquo team deployed in Boston
Comparing PRE- to POST-PROTECT ldquodeterrencerdquo improved
Additional real-world indicators from Boston
Boston boaters questions
ldquohas the Coast Guard recently acquired more boatsrdquo
POST-PROTECT Actual reports of illegal activity
55
5956
Field Tests Against Adversaries
Computational Game Theory in the Field
Game theory vs Random
21 days of patrol
Identical conditions
Random + Human
Not controlled
0
20
40
60
80
100 Miscellaneous
Drugs
Firearm Violations
0
5
10
15
20
Captures30 min
Warnings30 min
Violations30 min
Game Theory
Rand+Human
Controlled
59
Expert Evaluation
Example from ARMOR IRIS amp PROTECT
February 2009 Commendations
LAX Police (City of Los Angeles)
July 2011 Operational Excellence
Award (US Coast Guard Boston)
September 2011 Certificate of
Appreciation (Federal Air Marshals)
June 2013 Meritorious Team Commendation
from Commandant (US Coast Guard)
57
59
Summary Security Games
Decision aids based on computational game theory in daily use
Optimize limited security resources against adversaries
Applications yield research challenges Science of security games
Scale-up Incremental strategy generation amp Marginals
Uncertainty Integrate MDPs Robustness Quantal response
Current applications (wildlife security) Interdisciplinar challenge
Global challenges Merge planninglearning amp security games
58
59
Just the Beginning of ldquoSecurity Gamesrdquohellip
Our next steps
59
Thank you
to sponsors
StartupARMORWAY Game theory in the field
Follow on research amp applications
Privacy audits
(Sinha IJCAIrsquo13)
Software testing
(Kukreja ASErsquo13)
Sport event security
(Yin AAAIrsquo14)
Singapore train
(Varakantham IAAIrsquo13)
Exam questions
(Li IJCAIrsquo13)
59
THANK YOU
tambeuscedu
httpteamcoreuscedusecurity
60
5914
ARMOR Payoffs [2007]Previous Research Provides Payoffs in Security Game Domains
Target 1 Target 2
Defender 1 2 -1 -3 4
Defender 2 -3 3 3 -2
jq
ix
ijR
Xi Qj
maxMaximize defender
expected utility
5915
ARMOR MIP [2007]Solving for a Single Adversary Type
Term 1 Term 2
Defend1 2 -1 -3 4
Defend2 -3 1 3 -3
1 i
ixts
jq
ix
ijR
Xi Qj
max
1Qj
jq
ljq][ix
MqxCa ji
Xi
ij
1010
)1()(0
Maximize defender
expected utility
Defender strategy
Adversary strategy
Adversary best
response
119909119894
ARMORhellipthrows a digital cloak of invisibilityhellip
59
IRIS Federal Air Marshals Service [2009]Scale Up Number of Defender Strategies
1000 Flights 20 air marshals 1041
combinations
ARMOR out of memory
Strategy 1 Strategy 2 Strategy 3
Strateg
y 1
Strateg
y 2
Strateg
y 3
Strateg
y 4
Strateg
y 5
Strateg
y 6
Not enumerate all combinations
Branch and price Incremental strategy generation
Strategy 1 Strategy 2 Strategy 3
Strategy 1
Strategy 2
Strategy 3
Strategy 4
Strategy 5
Strategy 6
16
59
IRIS Scale Up Number of Defender Strategies [2009]
Small Support Set for Mixed Strategies
Small support set size
bull Most xi variables zero
x124=0239
x123=00
x135=00
Attack
1
Attack
2
Attack
hellip
Attack
1000
123 5-10 4-8 hellip -209
124 5-10 4-8 hellip -209
135 5-10 -95 hellip -209
hellip
hellip1041 rowsx378=0123
10]10[
)1()(0
11
max
jqx
MqxCa
qxts
qxR
i
ji
Xi
ij
Qj
j
i
i
jiij
Xi Qj
qx
1000 flights 20 air marshals
1041combinations
17
59
Target 3 Target 7
hellip hellip
Resource Sink
Best new pure strategy
Minimum cost network flow
IRIS Incremental Strategy Generation
Exploit Small Support
Attack 1 Attack 2 Attack Attack 6
124 5-10 4-8 hellip -209
378 -8 10 -810 hellip -810
hellip
500 rows
NOT 1041
Attack 1 Attack 2 Attackhellip Attack 6
124 5-10 4-8 hellip -209
Slave (LP Duality Theory)
Master
Converge
GLOBAL
OPTIMAL
Attack 1 Attack 2 Attackhellip Attack 6
124 5-10 4-8 hellip -209
378 -8 10 -810 hellip -810
18
Jain Kiekintveld
59
IRIS Deployed FAMS (2009-)
ldquohellipin 2011 the Military Operations Research Society selected a University
of Southern California project with FAMS on randomizing flight
schedules for the prestigious Rist Awardhelliprdquo
-R S Bray (TSA)
Transportation Security Subcommittee
US House of Representatives 2012
19
Significant change in operations
59
Networks Mumbai Police Checkpoints[2013]
150 edges 2 Checkpoints
150-choose-2 strategies
With V Conitzer 20
Jain
59
Networks Mumbai Police Checkpoints[2013]
Incremental Strategy Generation
With V Conitzer 21
Jain
Double oracle Converge to a global optimal
Path 1 Path 2
Checkpoint
strategy 15 -5 -1 1
Path 1 Path 2
Checkpoint
strategy 15 -5 -1 1
Checkpoint
strategy 2-5 5 2 -1
Defender oracle
Path 1 Path 2 Path 3
Checkpoint
strategy 15 -5 -1 1 -2 2
Checkpoint
strategy 2-5 5 1 -1 -2 2
Attacker oracle
Path 1 Path 2 Path 3
Checkpoint
strategy 15 -5 -1 1 -2 2
Checkpoint
strategy 2-5 5 1 -1 -2 2
59
Double Oracle[2013]Incremental Strategy Generation Exploit Small Support
22
150 edges 2 Checkpoints
Only six candidate edges
for checkpoints
Jain
5923
Mumbai Police Checkpoints[2013]Results of Scale-up
20416 Roads15 checkpoints
20 min
59
Double Oracle Social Cyber Networks[2013]
Incremental Strategy Generation
24
Social networkseg counter-insurgency
Sources
Intermediate
Nodes
Links
Targets
Cyber networks
Tsai
59
Outline ldquoSecurity Gamesrdquo Research
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
25
59
Port Security Threat Scenarios
US Ports $315 trillion economy
USS Cole after suicide attack
French oil tanker hit by small boat
Attack on a ferry
26
59
PROTECT Randomized Patrol Scheduling [2013]
27
Port Protection (Scale-up) and Ferries (Continuous Spacetime)
59
PROTECT Randomized Patrol Scheduling [2013]Port Protection (Scale-up) and Ferries (Continuous Spacetime)
28
59
Ferries Scale-up with Mobile Resources amp Moving TargetsTransition Graph Representation
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
29
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Ferries Scale-up with Mobile Resources amp Moving TargetsTransition Graph Representation
30
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
31
Patrols protect nearby ferry location Solve as done in ARMOR
Ferries Patrol RoutesExponential Numbers of Patrol Routes
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Patrols protect nearby ferry location Solve as done in ARMOR
Ferries Patrol RoutesExponential Numbers of Patrol Routes
32
Pr([(B5) (C 10) (C15)]) = 017
Pr([(B5) (C10) (B15)]) =013
NT variables
Pr([(A5) (A10) (B15)]) = 007
Pr([(A5) (A10) (A15)]) = 003
Jiang KiekintveldFang
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Variables NOT routes but probability flow over each segment
Ferries Scale-up Marginal Probabilities Over Segments
33
NT variables
Jiang KiekintveldFang
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
030
017
013
Obtain marginal probabilities over segments
010
007
003
N2T variablesExtract Pr([(B5) (C 10) (C15)]) = 017
Pr([(B5) (C10) (B15)]) =013
Ferries Scale-up with Marginals Over Separable SegmentsSignificant Speedup
34
NT variables
59
5936
Outline ldquoSecurity Gamesrdquo Research (2007-Now)
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
Evaluation II Real-world deployments
(Patience)
Evaluation I Scale up Handle uncertainty
59
TRUSTS Frequent adversary interaction games
Patrols Against Fare Evaders
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
017
013
010
010
37
59
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
38
Jiang Delle Fave
59
Markov Decision Problems in Security games
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
39
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution Jiang Delle Fave
5940
Urban Transportation Security
MDPs amp DEC-MDPs in Security Games
COPS LA Metro System(Against Opportunistic Crime)
STREETS Singapore Roads(Against Reckless Driving)
DEC-MDPs in Security Games Teamwork + Uncertainty
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
ZhangShieh
59
Uncertainty Space Algorithms
Bayesian and Robust Approaches
Adversary payoff uncertainty
Adversary observation amp
defender execution
uncertainty
Adversary rationality
uncertainty
RECON
BRASS
Monotonic Maximin
(Monotonic adversary)
41
URAC
Payoff interval
Not point
estimateGMC HUNTER
Bayesian
Robust
Yin Nguyen An
0
05
1
15
5 10
Def
end
ers
EU
Targets
ISG RECON URAC
59
Security Games Environmental Crime
amp Bounded Rationality
FisheryGulf of Mexico
Nakai Nam Theun
Forest Area Laos
WildlifeQueen Elizabeth National
ParkUganda
No patrols
Higher
density
Lower densityx
Yang Ford Brown
59
Uncertainty in Adversary Decision Bounded Rationality
Human Subjects as Poachers
59
Uncertainty in Adversary Decision[2009]
Human subjects Anchoring e-Optimality
-4
-3
-2
-1
0
1
Unobserved 5 Observations 20 Observations Unlimited
Average expected reward
DOBSS
Uniform
COBRA
ARMOR
ARMOR Outperforms uniform random
COBRA
MqxCaq
Xxxts
qxR
ji
Xi
ijj
Xi Qj
jiijqx
)1()()1(
|)|1()1(
max
ee
Anchoring
e-optimality
With Sarit Kraus 44
Pita
5945
Quantal Response Stochastic choice better choice more likely
Quantal Response Model of Adversary [2011] Not Maximize Expected Utility
T
j
jxadversary
EU
jxadversary
EU
j
e
eq
1
))((
))((
-3
-25
-2
-15
-1
-05
0
05
1
15
2
Payoff 1 Payoff 2 Payoff 3 Payoff 4
QuantalResponse
COBRA
ARMOR
Adversaryrsquos probability
of choosing target j
Yang
10
)(max1
1
)(
)(
t tt
defendT
jT
j
jxEU
jxEU
x
xKxts
jxEU
e
e
adversary
adversary
59
Uncertainty in Adversary Decision [2012]
Robust vs Modeling Adversaries
Robustness Bound loss to defender Not model attacker via QR
Defeating Robust Learned subjective utility
Robust wins Draw QR wins
42 52 6
Results on 100 games
SU-QR wins Draw Robust
13 8 1
Results on 22 games
MqxCa
qxR
ji
Xi
ij
jiij
Xi Qj
qx
)1()(0
max
β (Adversaryrsquos utility loss if
deviates from optimal) gt=
(Defenderrsquos utility loss due to
adversary deviation)
penaltyattackw
rewardattackw
probcapturewjaSEU
3
2
1)(
M
j
jxSEU
jxSEU
jadversary
adversary
e
eq
1
)(
)(
SU-QR wins Draw Robust
6 13 3
Results against security experts
With Sarit Kraus 46
5947
PAWS Protection Assistant for Wildlife Security[2014]Repeated Stackelberg Game
Simulation
Learn from crime data
Improve model
Defender calculates
mixed strategy
Defender executes
randomized patrols
Poachers attack targets
119890119878119864119880119894(w1w2w3)
119896 119890119878119864119880119896(w1w2w3)
Bayesian SUQR Heterogeneous Poachers
5948
PAWS Test April 2014
Trials in Queen Elizabeth National Park
Andrew Lemieux with rangers
on PAWS patrol in Uganda
59
Security Resource OptimizationEvaluating Deployed Security Systems Not Easy
Game theory Improvement over previous approaches
Previous Human schedulers or ldquosimple randomrdquo
49
Lab
Evaluation
Simulated
adversary
Human subject
adversaries
Field Evaluation
Patrol quality
Unpredictable Cover
Compare real schedules
Scheduling competition
Expert evaluation
Field Evaluation
Tests against adversaries
ldquoMock attackersrdquo
Capture rates of
real adversaries
59
Why Does Game Theory Perform Better
Weaknesses of Previous Methods
Human schedulers
Predictable patterns eg US Coast Guard
Scheduling effort amp cognitive burden
Simple random (eg dice roll)
Wrong weightscoverage eg officers to sparsely crowded terminals
No adversary reactions
Multiple deployments over multiple years without us forcing them
50
59
Lab Evaluation via Simulations
Example from IRIS (FAMS)
-10
-8
-6
-4
-2
0
2
4
6
50 150 250
Defe
nde
r E
xpe
cte
d u
tilit
y
Schedule Size
Uniform Weighted random 1 Weighted random 2 IRIS
51
59
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage
Patrols Before PROTECT Boston Patrols After PROTECT Boston
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Base Patrol Area
52
PROTECT (Coast Guard) 350 increase defender expected utility
59
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage for Less Effort
53
IRIS for FAMS Outperformed expert human over six monthsReportGAO-09-903T
ARMOR at LAX Savings of up to an hour a day in scheduling
59
Field Evaluation Human vs Game Theory Competition
Counter-terrorism Patrol Scheduling
90 officers on LA Metro Trains
Humans required significant effort
Worse schedules than game theory
Observerrsquos report on questions
54
3
35
4
45
5
55
Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Q9
Q10
Q11
Q12
Secu
rity
Sco
re
Human Game Theory
59
Field Test Against Adversaries Mock Attackers
Example from PROTECT
ldquoMock attackerrdquo team deployed in Boston
Comparing PRE- to POST-PROTECT ldquodeterrencerdquo improved
Additional real-world indicators from Boston
Boston boaters questions
ldquohas the Coast Guard recently acquired more boatsrdquo
POST-PROTECT Actual reports of illegal activity
55
5956
Field Tests Against Adversaries
Computational Game Theory in the Field
Game theory vs Random
21 days of patrol
Identical conditions
Random + Human
Not controlled
0
20
40
60
80
100 Miscellaneous
Drugs
Firearm Violations
0
5
10
15
20
Captures30 min
Warnings30 min
Violations30 min
Game Theory
Rand+Human
Controlled
59
Expert Evaluation
Example from ARMOR IRIS amp PROTECT
February 2009 Commendations
LAX Police (City of Los Angeles)
July 2011 Operational Excellence
Award (US Coast Guard Boston)
September 2011 Certificate of
Appreciation (Federal Air Marshals)
June 2013 Meritorious Team Commendation
from Commandant (US Coast Guard)
57
59
Summary Security Games
Decision aids based on computational game theory in daily use
Optimize limited security resources against adversaries
Applications yield research challenges Science of security games
Scale-up Incremental strategy generation amp Marginals
Uncertainty Integrate MDPs Robustness Quantal response
Current applications (wildlife security) Interdisciplinar challenge
Global challenges Merge planninglearning amp security games
58
59
Just the Beginning of ldquoSecurity Gamesrdquohellip
Our next steps
59
Thank you
to sponsors
StartupARMORWAY Game theory in the field
Follow on research amp applications
Privacy audits
(Sinha IJCAIrsquo13)
Software testing
(Kukreja ASErsquo13)
Sport event security
(Yin AAAIrsquo14)
Singapore train
(Varakantham IAAIrsquo13)
Exam questions
(Li IJCAIrsquo13)
59
THANK YOU
tambeuscedu
httpteamcoreuscedusecurity
60
5915
ARMOR MIP [2007]Solving for a Single Adversary Type
Term 1 Term 2
Defend1 2 -1 -3 4
Defend2 -3 1 3 -3
1 i
ixts
jq
ix
ijR
Xi Qj
max
1Qj
jq
ljq][ix
MqxCa ji
Xi
ij
1010
)1()(0
Maximize defender
expected utility
Defender strategy
Adversary strategy
Adversary best
response
119909119894
ARMORhellipthrows a digital cloak of invisibilityhellip
59
IRIS Federal Air Marshals Service [2009]Scale Up Number of Defender Strategies
1000 Flights 20 air marshals 1041
combinations
ARMOR out of memory
Strategy 1 Strategy 2 Strategy 3
Strateg
y 1
Strateg
y 2
Strateg
y 3
Strateg
y 4
Strateg
y 5
Strateg
y 6
Not enumerate all combinations
Branch and price Incremental strategy generation
Strategy 1 Strategy 2 Strategy 3
Strategy 1
Strategy 2
Strategy 3
Strategy 4
Strategy 5
Strategy 6
16
59
IRIS Scale Up Number of Defender Strategies [2009]
Small Support Set for Mixed Strategies
Small support set size
bull Most xi variables zero
x124=0239
x123=00
x135=00
Attack
1
Attack
2
Attack
hellip
Attack
1000
123 5-10 4-8 hellip -209
124 5-10 4-8 hellip -209
135 5-10 -95 hellip -209
hellip
hellip1041 rowsx378=0123
10]10[
)1()(0
11
max
jqx
MqxCa
qxts
qxR
i
ji
Xi
ij
Qj
j
i
i
jiij
Xi Qj
qx
1000 flights 20 air marshals
1041combinations
17
59
Target 3 Target 7
hellip hellip
Resource Sink
Best new pure strategy
Minimum cost network flow
IRIS Incremental Strategy Generation
Exploit Small Support
Attack 1 Attack 2 Attack Attack 6
124 5-10 4-8 hellip -209
378 -8 10 -810 hellip -810
hellip
500 rows
NOT 1041
Attack 1 Attack 2 Attackhellip Attack 6
124 5-10 4-8 hellip -209
Slave (LP Duality Theory)
Master
Converge
GLOBAL
OPTIMAL
Attack 1 Attack 2 Attackhellip Attack 6
124 5-10 4-8 hellip -209
378 -8 10 -810 hellip -810
18
Jain Kiekintveld
59
IRIS Deployed FAMS (2009-)
ldquohellipin 2011 the Military Operations Research Society selected a University
of Southern California project with FAMS on randomizing flight
schedules for the prestigious Rist Awardhelliprdquo
-R S Bray (TSA)
Transportation Security Subcommittee
US House of Representatives 2012
19
Significant change in operations
59
Networks Mumbai Police Checkpoints[2013]
150 edges 2 Checkpoints
150-choose-2 strategies
With V Conitzer 20
Jain
59
Networks Mumbai Police Checkpoints[2013]
Incremental Strategy Generation
With V Conitzer 21
Jain
Double oracle Converge to a global optimal
Path 1 Path 2
Checkpoint
strategy 15 -5 -1 1
Path 1 Path 2
Checkpoint
strategy 15 -5 -1 1
Checkpoint
strategy 2-5 5 2 -1
Defender oracle
Path 1 Path 2 Path 3
Checkpoint
strategy 15 -5 -1 1 -2 2
Checkpoint
strategy 2-5 5 1 -1 -2 2
Attacker oracle
Path 1 Path 2 Path 3
Checkpoint
strategy 15 -5 -1 1 -2 2
Checkpoint
strategy 2-5 5 1 -1 -2 2
59
Double Oracle[2013]Incremental Strategy Generation Exploit Small Support
22
150 edges 2 Checkpoints
Only six candidate edges
for checkpoints
Jain
5923
Mumbai Police Checkpoints[2013]Results of Scale-up
20416 Roads15 checkpoints
20 min
59
Double Oracle Social Cyber Networks[2013]
Incremental Strategy Generation
24
Social networkseg counter-insurgency
Sources
Intermediate
Nodes
Links
Targets
Cyber networks
Tsai
59
Outline ldquoSecurity Gamesrdquo Research
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
25
59
Port Security Threat Scenarios
US Ports $315 trillion economy
USS Cole after suicide attack
French oil tanker hit by small boat
Attack on a ferry
26
59
PROTECT Randomized Patrol Scheduling [2013]
27
Port Protection (Scale-up) and Ferries (Continuous Spacetime)
59
PROTECT Randomized Patrol Scheduling [2013]Port Protection (Scale-up) and Ferries (Continuous Spacetime)
28
59
Ferries Scale-up with Mobile Resources amp Moving TargetsTransition Graph Representation
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
29
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Ferries Scale-up with Mobile Resources amp Moving TargetsTransition Graph Representation
30
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
31
Patrols protect nearby ferry location Solve as done in ARMOR
Ferries Patrol RoutesExponential Numbers of Patrol Routes
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Patrols protect nearby ferry location Solve as done in ARMOR
Ferries Patrol RoutesExponential Numbers of Patrol Routes
32
Pr([(B5) (C 10) (C15)]) = 017
Pr([(B5) (C10) (B15)]) =013
NT variables
Pr([(A5) (A10) (B15)]) = 007
Pr([(A5) (A10) (A15)]) = 003
Jiang KiekintveldFang
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Variables NOT routes but probability flow over each segment
Ferries Scale-up Marginal Probabilities Over Segments
33
NT variables
Jiang KiekintveldFang
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
030
017
013
Obtain marginal probabilities over segments
010
007
003
N2T variablesExtract Pr([(B5) (C 10) (C15)]) = 017
Pr([(B5) (C10) (B15)]) =013
Ferries Scale-up with Marginals Over Separable SegmentsSignificant Speedup
34
NT variables
59
5936
Outline ldquoSecurity Gamesrdquo Research (2007-Now)
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
Evaluation II Real-world deployments
(Patience)
Evaluation I Scale up Handle uncertainty
59
TRUSTS Frequent adversary interaction games
Patrols Against Fare Evaders
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
017
013
010
010
37
59
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
38
Jiang Delle Fave
59
Markov Decision Problems in Security games
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
39
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution Jiang Delle Fave
5940
Urban Transportation Security
MDPs amp DEC-MDPs in Security Games
COPS LA Metro System(Against Opportunistic Crime)
STREETS Singapore Roads(Against Reckless Driving)
DEC-MDPs in Security Games Teamwork + Uncertainty
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
ZhangShieh
59
Uncertainty Space Algorithms
Bayesian and Robust Approaches
Adversary payoff uncertainty
Adversary observation amp
defender execution
uncertainty
Adversary rationality
uncertainty
RECON
BRASS
Monotonic Maximin
(Monotonic adversary)
41
URAC
Payoff interval
Not point
estimateGMC HUNTER
Bayesian
Robust
Yin Nguyen An
0
05
1
15
5 10
Def
end
ers
EU
Targets
ISG RECON URAC
59
Security Games Environmental Crime
amp Bounded Rationality
FisheryGulf of Mexico
Nakai Nam Theun
Forest Area Laos
WildlifeQueen Elizabeth National
ParkUganda
No patrols
Higher
density
Lower densityx
Yang Ford Brown
59
Uncertainty in Adversary Decision Bounded Rationality
Human Subjects as Poachers
59
Uncertainty in Adversary Decision[2009]
Human subjects Anchoring e-Optimality
-4
-3
-2
-1
0
1
Unobserved 5 Observations 20 Observations Unlimited
Average expected reward
DOBSS
Uniform
COBRA
ARMOR
ARMOR Outperforms uniform random
COBRA
MqxCaq
Xxxts
qxR
ji
Xi
ijj
Xi Qj
jiijqx
)1()()1(
|)|1()1(
max
ee
Anchoring
e-optimality
With Sarit Kraus 44
Pita
5945
Quantal Response Stochastic choice better choice more likely
Quantal Response Model of Adversary [2011] Not Maximize Expected Utility
T
j
jxadversary
EU
jxadversary
EU
j
e
eq
1
))((
))((
-3
-25
-2
-15
-1
-05
0
05
1
15
2
Payoff 1 Payoff 2 Payoff 3 Payoff 4
QuantalResponse
COBRA
ARMOR
Adversaryrsquos probability
of choosing target j
Yang
10
)(max1
1
)(
)(
t tt
defendT
jT
j
jxEU
jxEU
x
xKxts
jxEU
e
e
adversary
adversary
59
Uncertainty in Adversary Decision [2012]
Robust vs Modeling Adversaries
Robustness Bound loss to defender Not model attacker via QR
Defeating Robust Learned subjective utility
Robust wins Draw QR wins
42 52 6
Results on 100 games
SU-QR wins Draw Robust
13 8 1
Results on 22 games
MqxCa
qxR
ji
Xi
ij
jiij
Xi Qj
qx
)1()(0
max
β (Adversaryrsquos utility loss if
deviates from optimal) gt=
(Defenderrsquos utility loss due to
adversary deviation)
penaltyattackw
rewardattackw
probcapturewjaSEU
3
2
1)(
M
j
jxSEU
jxSEU
jadversary
adversary
e
eq
1
)(
)(
SU-QR wins Draw Robust
6 13 3
Results against security experts
With Sarit Kraus 46
5947
PAWS Protection Assistant for Wildlife Security[2014]Repeated Stackelberg Game
Simulation
Learn from crime data
Improve model
Defender calculates
mixed strategy
Defender executes
randomized patrols
Poachers attack targets
119890119878119864119880119894(w1w2w3)
119896 119890119878119864119880119896(w1w2w3)
Bayesian SUQR Heterogeneous Poachers
5948
PAWS Test April 2014
Trials in Queen Elizabeth National Park
Andrew Lemieux with rangers
on PAWS patrol in Uganda
59
Security Resource OptimizationEvaluating Deployed Security Systems Not Easy
Game theory Improvement over previous approaches
Previous Human schedulers or ldquosimple randomrdquo
49
Lab
Evaluation
Simulated
adversary
Human subject
adversaries
Field Evaluation
Patrol quality
Unpredictable Cover
Compare real schedules
Scheduling competition
Expert evaluation
Field Evaluation
Tests against adversaries
ldquoMock attackersrdquo
Capture rates of
real adversaries
59
Why Does Game Theory Perform Better
Weaknesses of Previous Methods
Human schedulers
Predictable patterns eg US Coast Guard
Scheduling effort amp cognitive burden
Simple random (eg dice roll)
Wrong weightscoverage eg officers to sparsely crowded terminals
No adversary reactions
Multiple deployments over multiple years without us forcing them
50
59
Lab Evaluation via Simulations
Example from IRIS (FAMS)
-10
-8
-6
-4
-2
0
2
4
6
50 150 250
Defe
nde
r E
xpe
cte
d u
tilit
y
Schedule Size
Uniform Weighted random 1 Weighted random 2 IRIS
51
59
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage
Patrols Before PROTECT Boston Patrols After PROTECT Boston
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Base Patrol Area
52
PROTECT (Coast Guard) 350 increase defender expected utility
59
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage for Less Effort
53
IRIS for FAMS Outperformed expert human over six monthsReportGAO-09-903T
ARMOR at LAX Savings of up to an hour a day in scheduling
59
Field Evaluation Human vs Game Theory Competition
Counter-terrorism Patrol Scheduling
90 officers on LA Metro Trains
Humans required significant effort
Worse schedules than game theory
Observerrsquos report on questions
54
3
35
4
45
5
55
Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Q9
Q10
Q11
Q12
Secu
rity
Sco
re
Human Game Theory
59
Field Test Against Adversaries Mock Attackers
Example from PROTECT
ldquoMock attackerrdquo team deployed in Boston
Comparing PRE- to POST-PROTECT ldquodeterrencerdquo improved
Additional real-world indicators from Boston
Boston boaters questions
ldquohas the Coast Guard recently acquired more boatsrdquo
POST-PROTECT Actual reports of illegal activity
55
5956
Field Tests Against Adversaries
Computational Game Theory in the Field
Game theory vs Random
21 days of patrol
Identical conditions
Random + Human
Not controlled
0
20
40
60
80
100 Miscellaneous
Drugs
Firearm Violations
0
5
10
15
20
Captures30 min
Warnings30 min
Violations30 min
Game Theory
Rand+Human
Controlled
59
Expert Evaluation
Example from ARMOR IRIS amp PROTECT
February 2009 Commendations
LAX Police (City of Los Angeles)
July 2011 Operational Excellence
Award (US Coast Guard Boston)
September 2011 Certificate of
Appreciation (Federal Air Marshals)
June 2013 Meritorious Team Commendation
from Commandant (US Coast Guard)
57
59
Summary Security Games
Decision aids based on computational game theory in daily use
Optimize limited security resources against adversaries
Applications yield research challenges Science of security games
Scale-up Incremental strategy generation amp Marginals
Uncertainty Integrate MDPs Robustness Quantal response
Current applications (wildlife security) Interdisciplinar challenge
Global challenges Merge planninglearning amp security games
58
59
Just the Beginning of ldquoSecurity Gamesrdquohellip
Our next steps
59
Thank you
to sponsors
StartupARMORWAY Game theory in the field
Follow on research amp applications
Privacy audits
(Sinha IJCAIrsquo13)
Software testing
(Kukreja ASErsquo13)
Sport event security
(Yin AAAIrsquo14)
Singapore train
(Varakantham IAAIrsquo13)
Exam questions
(Li IJCAIrsquo13)
59
THANK YOU
tambeuscedu
httpteamcoreuscedusecurity
60
59
IRIS Federal Air Marshals Service [2009]Scale Up Number of Defender Strategies
1000 Flights 20 air marshals 1041
combinations
ARMOR out of memory
Strategy 1 Strategy 2 Strategy 3
Strateg
y 1
Strateg
y 2
Strateg
y 3
Strateg
y 4
Strateg
y 5
Strateg
y 6
Not enumerate all combinations
Branch and price Incremental strategy generation
Strategy 1 Strategy 2 Strategy 3
Strategy 1
Strategy 2
Strategy 3
Strategy 4
Strategy 5
Strategy 6
16
59
IRIS Scale Up Number of Defender Strategies [2009]
Small Support Set for Mixed Strategies
Small support set size
bull Most xi variables zero
x124=0239
x123=00
x135=00
Attack
1
Attack
2
Attack
hellip
Attack
1000
123 5-10 4-8 hellip -209
124 5-10 4-8 hellip -209
135 5-10 -95 hellip -209
hellip
hellip1041 rowsx378=0123
10]10[
)1()(0
11
max
jqx
MqxCa
qxts
qxR
i
ji
Xi
ij
Qj
j
i
i
jiij
Xi Qj
qx
1000 flights 20 air marshals
1041combinations
17
59
Target 3 Target 7
hellip hellip
Resource Sink
Best new pure strategy
Minimum cost network flow
IRIS Incremental Strategy Generation
Exploit Small Support
Attack 1 Attack 2 Attack Attack 6
124 5-10 4-8 hellip -209
378 -8 10 -810 hellip -810
hellip
500 rows
NOT 1041
Attack 1 Attack 2 Attackhellip Attack 6
124 5-10 4-8 hellip -209
Slave (LP Duality Theory)
Master
Converge
GLOBAL
OPTIMAL
Attack 1 Attack 2 Attackhellip Attack 6
124 5-10 4-8 hellip -209
378 -8 10 -810 hellip -810
18
Jain Kiekintveld
59
IRIS Deployed FAMS (2009-)
ldquohellipin 2011 the Military Operations Research Society selected a University
of Southern California project with FAMS on randomizing flight
schedules for the prestigious Rist Awardhelliprdquo
-R S Bray (TSA)
Transportation Security Subcommittee
US House of Representatives 2012
19
Significant change in operations
59
Networks Mumbai Police Checkpoints[2013]
150 edges 2 Checkpoints
150-choose-2 strategies
With V Conitzer 20
Jain
59
Networks Mumbai Police Checkpoints[2013]
Incremental Strategy Generation
With V Conitzer 21
Jain
Double oracle Converge to a global optimal
Path 1 Path 2
Checkpoint
strategy 15 -5 -1 1
Path 1 Path 2
Checkpoint
strategy 15 -5 -1 1
Checkpoint
strategy 2-5 5 2 -1
Defender oracle
Path 1 Path 2 Path 3
Checkpoint
strategy 15 -5 -1 1 -2 2
Checkpoint
strategy 2-5 5 1 -1 -2 2
Attacker oracle
Path 1 Path 2 Path 3
Checkpoint
strategy 15 -5 -1 1 -2 2
Checkpoint
strategy 2-5 5 1 -1 -2 2
59
Double Oracle[2013]Incremental Strategy Generation Exploit Small Support
22
150 edges 2 Checkpoints
Only six candidate edges
for checkpoints
Jain
5923
Mumbai Police Checkpoints[2013]Results of Scale-up
20416 Roads15 checkpoints
20 min
59
Double Oracle Social Cyber Networks[2013]
Incremental Strategy Generation
24
Social networkseg counter-insurgency
Sources
Intermediate
Nodes
Links
Targets
Cyber networks
Tsai
59
Outline ldquoSecurity Gamesrdquo Research
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
25
59
Port Security Threat Scenarios
US Ports $315 trillion economy
USS Cole after suicide attack
French oil tanker hit by small boat
Attack on a ferry
26
59
PROTECT Randomized Patrol Scheduling [2013]
27
Port Protection (Scale-up) and Ferries (Continuous Spacetime)
59
PROTECT Randomized Patrol Scheduling [2013]Port Protection (Scale-up) and Ferries (Continuous Spacetime)
28
59
Ferries Scale-up with Mobile Resources amp Moving TargetsTransition Graph Representation
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
29
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Ferries Scale-up with Mobile Resources amp Moving TargetsTransition Graph Representation
30
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
31
Patrols protect nearby ferry location Solve as done in ARMOR
Ferries Patrol RoutesExponential Numbers of Patrol Routes
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Patrols protect nearby ferry location Solve as done in ARMOR
Ferries Patrol RoutesExponential Numbers of Patrol Routes
32
Pr([(B5) (C 10) (C15)]) = 017
Pr([(B5) (C10) (B15)]) =013
NT variables
Pr([(A5) (A10) (B15)]) = 007
Pr([(A5) (A10) (A15)]) = 003
Jiang KiekintveldFang
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Variables NOT routes but probability flow over each segment
Ferries Scale-up Marginal Probabilities Over Segments
33
NT variables
Jiang KiekintveldFang
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
030
017
013
Obtain marginal probabilities over segments
010
007
003
N2T variablesExtract Pr([(B5) (C 10) (C15)]) = 017
Pr([(B5) (C10) (B15)]) =013
Ferries Scale-up with Marginals Over Separable SegmentsSignificant Speedup
34
NT variables
59
5936
Outline ldquoSecurity Gamesrdquo Research (2007-Now)
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
Evaluation II Real-world deployments
(Patience)
Evaluation I Scale up Handle uncertainty
59
TRUSTS Frequent adversary interaction games
Patrols Against Fare Evaders
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
017
013
010
010
37
59
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
38
Jiang Delle Fave
59
Markov Decision Problems in Security games
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
39
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution Jiang Delle Fave
5940
Urban Transportation Security
MDPs amp DEC-MDPs in Security Games
COPS LA Metro System(Against Opportunistic Crime)
STREETS Singapore Roads(Against Reckless Driving)
DEC-MDPs in Security Games Teamwork + Uncertainty
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
ZhangShieh
59
Uncertainty Space Algorithms
Bayesian and Robust Approaches
Adversary payoff uncertainty
Adversary observation amp
defender execution
uncertainty
Adversary rationality
uncertainty
RECON
BRASS
Monotonic Maximin
(Monotonic adversary)
41
URAC
Payoff interval
Not point
estimateGMC HUNTER
Bayesian
Robust
Yin Nguyen An
0
05
1
15
5 10
Def
end
ers
EU
Targets
ISG RECON URAC
59
Security Games Environmental Crime
amp Bounded Rationality
FisheryGulf of Mexico
Nakai Nam Theun
Forest Area Laos
WildlifeQueen Elizabeth National
ParkUganda
No patrols
Higher
density
Lower densityx
Yang Ford Brown
59
Uncertainty in Adversary Decision Bounded Rationality
Human Subjects as Poachers
59
Uncertainty in Adversary Decision[2009]
Human subjects Anchoring e-Optimality
-4
-3
-2
-1
0
1
Unobserved 5 Observations 20 Observations Unlimited
Average expected reward
DOBSS
Uniform
COBRA
ARMOR
ARMOR Outperforms uniform random
COBRA
MqxCaq
Xxxts
qxR
ji
Xi
ijj
Xi Qj
jiijqx
)1()()1(
|)|1()1(
max
ee
Anchoring
e-optimality
With Sarit Kraus 44
Pita
5945
Quantal Response Stochastic choice better choice more likely
Quantal Response Model of Adversary [2011] Not Maximize Expected Utility
T
j
jxadversary
EU
jxadversary
EU
j
e
eq
1
))((
))((
-3
-25
-2
-15
-1
-05
0
05
1
15
2
Payoff 1 Payoff 2 Payoff 3 Payoff 4
QuantalResponse
COBRA
ARMOR
Adversaryrsquos probability
of choosing target j
Yang
10
)(max1
1
)(
)(
t tt
defendT
jT
j
jxEU
jxEU
x
xKxts
jxEU
e
e
adversary
adversary
59
Uncertainty in Adversary Decision [2012]
Robust vs Modeling Adversaries
Robustness Bound loss to defender Not model attacker via QR
Defeating Robust Learned subjective utility
Robust wins Draw QR wins
42 52 6
Results on 100 games
SU-QR wins Draw Robust
13 8 1
Results on 22 games
MqxCa
qxR
ji
Xi
ij
jiij
Xi Qj
qx
)1()(0
max
β (Adversaryrsquos utility loss if
deviates from optimal) gt=
(Defenderrsquos utility loss due to
adversary deviation)
penaltyattackw
rewardattackw
probcapturewjaSEU
3
2
1)(
M
j
jxSEU
jxSEU
jadversary
adversary
e
eq
1
)(
)(
SU-QR wins Draw Robust
6 13 3
Results against security experts
With Sarit Kraus 46
5947
PAWS Protection Assistant for Wildlife Security[2014]Repeated Stackelberg Game
Simulation
Learn from crime data
Improve model
Defender calculates
mixed strategy
Defender executes
randomized patrols
Poachers attack targets
119890119878119864119880119894(w1w2w3)
119896 119890119878119864119880119896(w1w2w3)
Bayesian SUQR Heterogeneous Poachers
5948
PAWS Test April 2014
Trials in Queen Elizabeth National Park
Andrew Lemieux with rangers
on PAWS patrol in Uganda
59
Security Resource OptimizationEvaluating Deployed Security Systems Not Easy
Game theory Improvement over previous approaches
Previous Human schedulers or ldquosimple randomrdquo
49
Lab
Evaluation
Simulated
adversary
Human subject
adversaries
Field Evaluation
Patrol quality
Unpredictable Cover
Compare real schedules
Scheduling competition
Expert evaluation
Field Evaluation
Tests against adversaries
ldquoMock attackersrdquo
Capture rates of
real adversaries
59
Why Does Game Theory Perform Better
Weaknesses of Previous Methods
Human schedulers
Predictable patterns eg US Coast Guard
Scheduling effort amp cognitive burden
Simple random (eg dice roll)
Wrong weightscoverage eg officers to sparsely crowded terminals
No adversary reactions
Multiple deployments over multiple years without us forcing them
50
59
Lab Evaluation via Simulations
Example from IRIS (FAMS)
-10
-8
-6
-4
-2
0
2
4
6
50 150 250
Defe
nde
r E
xpe
cte
d u
tilit
y
Schedule Size
Uniform Weighted random 1 Weighted random 2 IRIS
51
59
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage
Patrols Before PROTECT Boston Patrols After PROTECT Boston
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Base Patrol Area
52
PROTECT (Coast Guard) 350 increase defender expected utility
59
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage for Less Effort
53
IRIS for FAMS Outperformed expert human over six monthsReportGAO-09-903T
ARMOR at LAX Savings of up to an hour a day in scheduling
59
Field Evaluation Human vs Game Theory Competition
Counter-terrorism Patrol Scheduling
90 officers on LA Metro Trains
Humans required significant effort
Worse schedules than game theory
Observerrsquos report on questions
54
3
35
4
45
5
55
Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Q9
Q10
Q11
Q12
Secu
rity
Sco
re
Human Game Theory
59
Field Test Against Adversaries Mock Attackers
Example from PROTECT
ldquoMock attackerrdquo team deployed in Boston
Comparing PRE- to POST-PROTECT ldquodeterrencerdquo improved
Additional real-world indicators from Boston
Boston boaters questions
ldquohas the Coast Guard recently acquired more boatsrdquo
POST-PROTECT Actual reports of illegal activity
55
5956
Field Tests Against Adversaries
Computational Game Theory in the Field
Game theory vs Random
21 days of patrol
Identical conditions
Random + Human
Not controlled
0
20
40
60
80
100 Miscellaneous
Drugs
Firearm Violations
0
5
10
15
20
Captures30 min
Warnings30 min
Violations30 min
Game Theory
Rand+Human
Controlled
59
Expert Evaluation
Example from ARMOR IRIS amp PROTECT
February 2009 Commendations
LAX Police (City of Los Angeles)
July 2011 Operational Excellence
Award (US Coast Guard Boston)
September 2011 Certificate of
Appreciation (Federal Air Marshals)
June 2013 Meritorious Team Commendation
from Commandant (US Coast Guard)
57
59
Summary Security Games
Decision aids based on computational game theory in daily use
Optimize limited security resources against adversaries
Applications yield research challenges Science of security games
Scale-up Incremental strategy generation amp Marginals
Uncertainty Integrate MDPs Robustness Quantal response
Current applications (wildlife security) Interdisciplinar challenge
Global challenges Merge planninglearning amp security games
58
59
Just the Beginning of ldquoSecurity Gamesrdquohellip
Our next steps
59
Thank you
to sponsors
StartupARMORWAY Game theory in the field
Follow on research amp applications
Privacy audits
(Sinha IJCAIrsquo13)
Software testing
(Kukreja ASErsquo13)
Sport event security
(Yin AAAIrsquo14)
Singapore train
(Varakantham IAAIrsquo13)
Exam questions
(Li IJCAIrsquo13)
59
THANK YOU
tambeuscedu
httpteamcoreuscedusecurity
60
59
IRIS Scale Up Number of Defender Strategies [2009]
Small Support Set for Mixed Strategies
Small support set size
bull Most xi variables zero
x124=0239
x123=00
x135=00
Attack
1
Attack
2
Attack
hellip
Attack
1000
123 5-10 4-8 hellip -209
124 5-10 4-8 hellip -209
135 5-10 -95 hellip -209
hellip
hellip1041 rowsx378=0123
10]10[
)1()(0
11
max
jqx
MqxCa
qxts
qxR
i
ji
Xi
ij
Qj
j
i
i
jiij
Xi Qj
qx
1000 flights 20 air marshals
1041combinations
17
59
Target 3 Target 7
hellip hellip
Resource Sink
Best new pure strategy
Minimum cost network flow
IRIS Incremental Strategy Generation
Exploit Small Support
Attack 1 Attack 2 Attack Attack 6
124 5-10 4-8 hellip -209
378 -8 10 -810 hellip -810
hellip
500 rows
NOT 1041
Attack 1 Attack 2 Attackhellip Attack 6
124 5-10 4-8 hellip -209
Slave (LP Duality Theory)
Master
Converge
GLOBAL
OPTIMAL
Attack 1 Attack 2 Attackhellip Attack 6
124 5-10 4-8 hellip -209
378 -8 10 -810 hellip -810
18
Jain Kiekintveld
59
IRIS Deployed FAMS (2009-)
ldquohellipin 2011 the Military Operations Research Society selected a University
of Southern California project with FAMS on randomizing flight
schedules for the prestigious Rist Awardhelliprdquo
-R S Bray (TSA)
Transportation Security Subcommittee
US House of Representatives 2012
19
Significant change in operations
59
Networks Mumbai Police Checkpoints[2013]
150 edges 2 Checkpoints
150-choose-2 strategies
With V Conitzer 20
Jain
59
Networks Mumbai Police Checkpoints[2013]
Incremental Strategy Generation
With V Conitzer 21
Jain
Double oracle Converge to a global optimal
Path 1 Path 2
Checkpoint
strategy 15 -5 -1 1
Path 1 Path 2
Checkpoint
strategy 15 -5 -1 1
Checkpoint
strategy 2-5 5 2 -1
Defender oracle
Path 1 Path 2 Path 3
Checkpoint
strategy 15 -5 -1 1 -2 2
Checkpoint
strategy 2-5 5 1 -1 -2 2
Attacker oracle
Path 1 Path 2 Path 3
Checkpoint
strategy 15 -5 -1 1 -2 2
Checkpoint
strategy 2-5 5 1 -1 -2 2
59
Double Oracle[2013]Incremental Strategy Generation Exploit Small Support
22
150 edges 2 Checkpoints
Only six candidate edges
for checkpoints
Jain
5923
Mumbai Police Checkpoints[2013]Results of Scale-up
20416 Roads15 checkpoints
20 min
59
Double Oracle Social Cyber Networks[2013]
Incremental Strategy Generation
24
Social networkseg counter-insurgency
Sources
Intermediate
Nodes
Links
Targets
Cyber networks
Tsai
59
Outline ldquoSecurity Gamesrdquo Research
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
25
59
Port Security Threat Scenarios
US Ports $315 trillion economy
USS Cole after suicide attack
French oil tanker hit by small boat
Attack on a ferry
26
59
PROTECT Randomized Patrol Scheduling [2013]
27
Port Protection (Scale-up) and Ferries (Continuous Spacetime)
59
PROTECT Randomized Patrol Scheduling [2013]Port Protection (Scale-up) and Ferries (Continuous Spacetime)
28
59
Ferries Scale-up with Mobile Resources amp Moving TargetsTransition Graph Representation
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
29
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Ferries Scale-up with Mobile Resources amp Moving TargetsTransition Graph Representation
30
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
31
Patrols protect nearby ferry location Solve as done in ARMOR
Ferries Patrol RoutesExponential Numbers of Patrol Routes
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Patrols protect nearby ferry location Solve as done in ARMOR
Ferries Patrol RoutesExponential Numbers of Patrol Routes
32
Pr([(B5) (C 10) (C15)]) = 017
Pr([(B5) (C10) (B15)]) =013
NT variables
Pr([(A5) (A10) (B15)]) = 007
Pr([(A5) (A10) (A15)]) = 003
Jiang KiekintveldFang
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Variables NOT routes but probability flow over each segment
Ferries Scale-up Marginal Probabilities Over Segments
33
NT variables
Jiang KiekintveldFang
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
030
017
013
Obtain marginal probabilities over segments
010
007
003
N2T variablesExtract Pr([(B5) (C 10) (C15)]) = 017
Pr([(B5) (C10) (B15)]) =013
Ferries Scale-up with Marginals Over Separable SegmentsSignificant Speedup
34
NT variables
59
5936
Outline ldquoSecurity Gamesrdquo Research (2007-Now)
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
Evaluation II Real-world deployments
(Patience)
Evaluation I Scale up Handle uncertainty
59
TRUSTS Frequent adversary interaction games
Patrols Against Fare Evaders
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
017
013
010
010
37
59
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
38
Jiang Delle Fave
59
Markov Decision Problems in Security games
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
39
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution Jiang Delle Fave
5940
Urban Transportation Security
MDPs amp DEC-MDPs in Security Games
COPS LA Metro System(Against Opportunistic Crime)
STREETS Singapore Roads(Against Reckless Driving)
DEC-MDPs in Security Games Teamwork + Uncertainty
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
ZhangShieh
59
Uncertainty Space Algorithms
Bayesian and Robust Approaches
Adversary payoff uncertainty
Adversary observation amp
defender execution
uncertainty
Adversary rationality
uncertainty
RECON
BRASS
Monotonic Maximin
(Monotonic adversary)
41
URAC
Payoff interval
Not point
estimateGMC HUNTER
Bayesian
Robust
Yin Nguyen An
0
05
1
15
5 10
Def
end
ers
EU
Targets
ISG RECON URAC
59
Security Games Environmental Crime
amp Bounded Rationality
FisheryGulf of Mexico
Nakai Nam Theun
Forest Area Laos
WildlifeQueen Elizabeth National
ParkUganda
No patrols
Higher
density
Lower densityx
Yang Ford Brown
59
Uncertainty in Adversary Decision Bounded Rationality
Human Subjects as Poachers
59
Uncertainty in Adversary Decision[2009]
Human subjects Anchoring e-Optimality
-4
-3
-2
-1
0
1
Unobserved 5 Observations 20 Observations Unlimited
Average expected reward
DOBSS
Uniform
COBRA
ARMOR
ARMOR Outperforms uniform random
COBRA
MqxCaq
Xxxts
qxR
ji
Xi
ijj
Xi Qj
jiijqx
)1()()1(
|)|1()1(
max
ee
Anchoring
e-optimality
With Sarit Kraus 44
Pita
5945
Quantal Response Stochastic choice better choice more likely
Quantal Response Model of Adversary [2011] Not Maximize Expected Utility
T
j
jxadversary
EU
jxadversary
EU
j
e
eq
1
))((
))((
-3
-25
-2
-15
-1
-05
0
05
1
15
2
Payoff 1 Payoff 2 Payoff 3 Payoff 4
QuantalResponse
COBRA
ARMOR
Adversaryrsquos probability
of choosing target j
Yang
10
)(max1
1
)(
)(
t tt
defendT
jT
j
jxEU
jxEU
x
xKxts
jxEU
e
e
adversary
adversary
59
Uncertainty in Adversary Decision [2012]
Robust vs Modeling Adversaries
Robustness Bound loss to defender Not model attacker via QR
Defeating Robust Learned subjective utility
Robust wins Draw QR wins
42 52 6
Results on 100 games
SU-QR wins Draw Robust
13 8 1
Results on 22 games
MqxCa
qxR
ji
Xi
ij
jiij
Xi Qj
qx
)1()(0
max
β (Adversaryrsquos utility loss if
deviates from optimal) gt=
(Defenderrsquos utility loss due to
adversary deviation)
penaltyattackw
rewardattackw
probcapturewjaSEU
3
2
1)(
M
j
jxSEU
jxSEU
jadversary
adversary
e
eq
1
)(
)(
SU-QR wins Draw Robust
6 13 3
Results against security experts
With Sarit Kraus 46
5947
PAWS Protection Assistant for Wildlife Security[2014]Repeated Stackelberg Game
Simulation
Learn from crime data
Improve model
Defender calculates
mixed strategy
Defender executes
randomized patrols
Poachers attack targets
119890119878119864119880119894(w1w2w3)
119896 119890119878119864119880119896(w1w2w3)
Bayesian SUQR Heterogeneous Poachers
5948
PAWS Test April 2014
Trials in Queen Elizabeth National Park
Andrew Lemieux with rangers
on PAWS patrol in Uganda
59
Security Resource OptimizationEvaluating Deployed Security Systems Not Easy
Game theory Improvement over previous approaches
Previous Human schedulers or ldquosimple randomrdquo
49
Lab
Evaluation
Simulated
adversary
Human subject
adversaries
Field Evaluation
Patrol quality
Unpredictable Cover
Compare real schedules
Scheduling competition
Expert evaluation
Field Evaluation
Tests against adversaries
ldquoMock attackersrdquo
Capture rates of
real adversaries
59
Why Does Game Theory Perform Better
Weaknesses of Previous Methods
Human schedulers
Predictable patterns eg US Coast Guard
Scheduling effort amp cognitive burden
Simple random (eg dice roll)
Wrong weightscoverage eg officers to sparsely crowded terminals
No adversary reactions
Multiple deployments over multiple years without us forcing them
50
59
Lab Evaluation via Simulations
Example from IRIS (FAMS)
-10
-8
-6
-4
-2
0
2
4
6
50 150 250
Defe
nde
r E
xpe
cte
d u
tilit
y
Schedule Size
Uniform Weighted random 1 Weighted random 2 IRIS
51
59
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage
Patrols Before PROTECT Boston Patrols After PROTECT Boston
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Base Patrol Area
52
PROTECT (Coast Guard) 350 increase defender expected utility
59
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage for Less Effort
53
IRIS for FAMS Outperformed expert human over six monthsReportGAO-09-903T
ARMOR at LAX Savings of up to an hour a day in scheduling
59
Field Evaluation Human vs Game Theory Competition
Counter-terrorism Patrol Scheduling
90 officers on LA Metro Trains
Humans required significant effort
Worse schedules than game theory
Observerrsquos report on questions
54
3
35
4
45
5
55
Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Q9
Q10
Q11
Q12
Secu
rity
Sco
re
Human Game Theory
59
Field Test Against Adversaries Mock Attackers
Example from PROTECT
ldquoMock attackerrdquo team deployed in Boston
Comparing PRE- to POST-PROTECT ldquodeterrencerdquo improved
Additional real-world indicators from Boston
Boston boaters questions
ldquohas the Coast Guard recently acquired more boatsrdquo
POST-PROTECT Actual reports of illegal activity
55
5956
Field Tests Against Adversaries
Computational Game Theory in the Field
Game theory vs Random
21 days of patrol
Identical conditions
Random + Human
Not controlled
0
20
40
60
80
100 Miscellaneous
Drugs
Firearm Violations
0
5
10
15
20
Captures30 min
Warnings30 min
Violations30 min
Game Theory
Rand+Human
Controlled
59
Expert Evaluation
Example from ARMOR IRIS amp PROTECT
February 2009 Commendations
LAX Police (City of Los Angeles)
July 2011 Operational Excellence
Award (US Coast Guard Boston)
September 2011 Certificate of
Appreciation (Federal Air Marshals)
June 2013 Meritorious Team Commendation
from Commandant (US Coast Guard)
57
59
Summary Security Games
Decision aids based on computational game theory in daily use
Optimize limited security resources against adversaries
Applications yield research challenges Science of security games
Scale-up Incremental strategy generation amp Marginals
Uncertainty Integrate MDPs Robustness Quantal response
Current applications (wildlife security) Interdisciplinar challenge
Global challenges Merge planninglearning amp security games
58
59
Just the Beginning of ldquoSecurity Gamesrdquohellip
Our next steps
59
Thank you
to sponsors
StartupARMORWAY Game theory in the field
Follow on research amp applications
Privacy audits
(Sinha IJCAIrsquo13)
Software testing
(Kukreja ASErsquo13)
Sport event security
(Yin AAAIrsquo14)
Singapore train
(Varakantham IAAIrsquo13)
Exam questions
(Li IJCAIrsquo13)
59
THANK YOU
tambeuscedu
httpteamcoreuscedusecurity
60
59
Target 3 Target 7
hellip hellip
Resource Sink
Best new pure strategy
Minimum cost network flow
IRIS Incremental Strategy Generation
Exploit Small Support
Attack 1 Attack 2 Attack Attack 6
124 5-10 4-8 hellip -209
378 -8 10 -810 hellip -810
hellip
500 rows
NOT 1041
Attack 1 Attack 2 Attackhellip Attack 6
124 5-10 4-8 hellip -209
Slave (LP Duality Theory)
Master
Converge
GLOBAL
OPTIMAL
Attack 1 Attack 2 Attackhellip Attack 6
124 5-10 4-8 hellip -209
378 -8 10 -810 hellip -810
18
Jain Kiekintveld
59
IRIS Deployed FAMS (2009-)
ldquohellipin 2011 the Military Operations Research Society selected a University
of Southern California project with FAMS on randomizing flight
schedules for the prestigious Rist Awardhelliprdquo
-R S Bray (TSA)
Transportation Security Subcommittee
US House of Representatives 2012
19
Significant change in operations
59
Networks Mumbai Police Checkpoints[2013]
150 edges 2 Checkpoints
150-choose-2 strategies
With V Conitzer 20
Jain
59
Networks Mumbai Police Checkpoints[2013]
Incremental Strategy Generation
With V Conitzer 21
Jain
Double oracle Converge to a global optimal
Path 1 Path 2
Checkpoint
strategy 15 -5 -1 1
Path 1 Path 2
Checkpoint
strategy 15 -5 -1 1
Checkpoint
strategy 2-5 5 2 -1
Defender oracle
Path 1 Path 2 Path 3
Checkpoint
strategy 15 -5 -1 1 -2 2
Checkpoint
strategy 2-5 5 1 -1 -2 2
Attacker oracle
Path 1 Path 2 Path 3
Checkpoint
strategy 15 -5 -1 1 -2 2
Checkpoint
strategy 2-5 5 1 -1 -2 2
59
Double Oracle[2013]Incremental Strategy Generation Exploit Small Support
22
150 edges 2 Checkpoints
Only six candidate edges
for checkpoints
Jain
5923
Mumbai Police Checkpoints[2013]Results of Scale-up
20416 Roads15 checkpoints
20 min
59
Double Oracle Social Cyber Networks[2013]
Incremental Strategy Generation
24
Social networkseg counter-insurgency
Sources
Intermediate
Nodes
Links
Targets
Cyber networks
Tsai
59
Outline ldquoSecurity Gamesrdquo Research
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
25
59
Port Security Threat Scenarios
US Ports $315 trillion economy
USS Cole after suicide attack
French oil tanker hit by small boat
Attack on a ferry
26
59
PROTECT Randomized Patrol Scheduling [2013]
27
Port Protection (Scale-up) and Ferries (Continuous Spacetime)
59
PROTECT Randomized Patrol Scheduling [2013]Port Protection (Scale-up) and Ferries (Continuous Spacetime)
28
59
Ferries Scale-up with Mobile Resources amp Moving TargetsTransition Graph Representation
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
29
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Ferries Scale-up with Mobile Resources amp Moving TargetsTransition Graph Representation
30
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
31
Patrols protect nearby ferry location Solve as done in ARMOR
Ferries Patrol RoutesExponential Numbers of Patrol Routes
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Patrols protect nearby ferry location Solve as done in ARMOR
Ferries Patrol RoutesExponential Numbers of Patrol Routes
32
Pr([(B5) (C 10) (C15)]) = 017
Pr([(B5) (C10) (B15)]) =013
NT variables
Pr([(A5) (A10) (B15)]) = 007
Pr([(A5) (A10) (A15)]) = 003
Jiang KiekintveldFang
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Variables NOT routes but probability flow over each segment
Ferries Scale-up Marginal Probabilities Over Segments
33
NT variables
Jiang KiekintveldFang
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
030
017
013
Obtain marginal probabilities over segments
010
007
003
N2T variablesExtract Pr([(B5) (C 10) (C15)]) = 017
Pr([(B5) (C10) (B15)]) =013
Ferries Scale-up with Marginals Over Separable SegmentsSignificant Speedup
34
NT variables
59
5936
Outline ldquoSecurity Gamesrdquo Research (2007-Now)
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
Evaluation II Real-world deployments
(Patience)
Evaluation I Scale up Handle uncertainty
59
TRUSTS Frequent adversary interaction games
Patrols Against Fare Evaders
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
017
013
010
010
37
59
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
38
Jiang Delle Fave
59
Markov Decision Problems in Security games
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
39
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution Jiang Delle Fave
5940
Urban Transportation Security
MDPs amp DEC-MDPs in Security Games
COPS LA Metro System(Against Opportunistic Crime)
STREETS Singapore Roads(Against Reckless Driving)
DEC-MDPs in Security Games Teamwork + Uncertainty
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
ZhangShieh
59
Uncertainty Space Algorithms
Bayesian and Robust Approaches
Adversary payoff uncertainty
Adversary observation amp
defender execution
uncertainty
Adversary rationality
uncertainty
RECON
BRASS
Monotonic Maximin
(Monotonic adversary)
41
URAC
Payoff interval
Not point
estimateGMC HUNTER
Bayesian
Robust
Yin Nguyen An
0
05
1
15
5 10
Def
end
ers
EU
Targets
ISG RECON URAC
59
Security Games Environmental Crime
amp Bounded Rationality
FisheryGulf of Mexico
Nakai Nam Theun
Forest Area Laos
WildlifeQueen Elizabeth National
ParkUganda
No patrols
Higher
density
Lower densityx
Yang Ford Brown
59
Uncertainty in Adversary Decision Bounded Rationality
Human Subjects as Poachers
59
Uncertainty in Adversary Decision[2009]
Human subjects Anchoring e-Optimality
-4
-3
-2
-1
0
1
Unobserved 5 Observations 20 Observations Unlimited
Average expected reward
DOBSS
Uniform
COBRA
ARMOR
ARMOR Outperforms uniform random
COBRA
MqxCaq
Xxxts
qxR
ji
Xi
ijj
Xi Qj
jiijqx
)1()()1(
|)|1()1(
max
ee
Anchoring
e-optimality
With Sarit Kraus 44
Pita
5945
Quantal Response Stochastic choice better choice more likely
Quantal Response Model of Adversary [2011] Not Maximize Expected Utility
T
j
jxadversary
EU
jxadversary
EU
j
e
eq
1
))((
))((
-3
-25
-2
-15
-1
-05
0
05
1
15
2
Payoff 1 Payoff 2 Payoff 3 Payoff 4
QuantalResponse
COBRA
ARMOR
Adversaryrsquos probability
of choosing target j
Yang
10
)(max1
1
)(
)(
t tt
defendT
jT
j
jxEU
jxEU
x
xKxts
jxEU
e
e
adversary
adversary
59
Uncertainty in Adversary Decision [2012]
Robust vs Modeling Adversaries
Robustness Bound loss to defender Not model attacker via QR
Defeating Robust Learned subjective utility
Robust wins Draw QR wins
42 52 6
Results on 100 games
SU-QR wins Draw Robust
13 8 1
Results on 22 games
MqxCa
qxR
ji
Xi
ij
jiij
Xi Qj
qx
)1()(0
max
β (Adversaryrsquos utility loss if
deviates from optimal) gt=
(Defenderrsquos utility loss due to
adversary deviation)
penaltyattackw
rewardattackw
probcapturewjaSEU
3
2
1)(
M
j
jxSEU
jxSEU
jadversary
adversary
e
eq
1
)(
)(
SU-QR wins Draw Robust
6 13 3
Results against security experts
With Sarit Kraus 46
5947
PAWS Protection Assistant for Wildlife Security[2014]Repeated Stackelberg Game
Simulation
Learn from crime data
Improve model
Defender calculates
mixed strategy
Defender executes
randomized patrols
Poachers attack targets
119890119878119864119880119894(w1w2w3)
119896 119890119878119864119880119896(w1w2w3)
Bayesian SUQR Heterogeneous Poachers
5948
PAWS Test April 2014
Trials in Queen Elizabeth National Park
Andrew Lemieux with rangers
on PAWS patrol in Uganda
59
Security Resource OptimizationEvaluating Deployed Security Systems Not Easy
Game theory Improvement over previous approaches
Previous Human schedulers or ldquosimple randomrdquo
49
Lab
Evaluation
Simulated
adversary
Human subject
adversaries
Field Evaluation
Patrol quality
Unpredictable Cover
Compare real schedules
Scheduling competition
Expert evaluation
Field Evaluation
Tests against adversaries
ldquoMock attackersrdquo
Capture rates of
real adversaries
59
Why Does Game Theory Perform Better
Weaknesses of Previous Methods
Human schedulers
Predictable patterns eg US Coast Guard
Scheduling effort amp cognitive burden
Simple random (eg dice roll)
Wrong weightscoverage eg officers to sparsely crowded terminals
No adversary reactions
Multiple deployments over multiple years without us forcing them
50
59
Lab Evaluation via Simulations
Example from IRIS (FAMS)
-10
-8
-6
-4
-2
0
2
4
6
50 150 250
Defe
nde
r E
xpe
cte
d u
tilit
y
Schedule Size
Uniform Weighted random 1 Weighted random 2 IRIS
51
59
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage
Patrols Before PROTECT Boston Patrols After PROTECT Boston
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Base Patrol Area
52
PROTECT (Coast Guard) 350 increase defender expected utility
59
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage for Less Effort
53
IRIS for FAMS Outperformed expert human over six monthsReportGAO-09-903T
ARMOR at LAX Savings of up to an hour a day in scheduling
59
Field Evaluation Human vs Game Theory Competition
Counter-terrorism Patrol Scheduling
90 officers on LA Metro Trains
Humans required significant effort
Worse schedules than game theory
Observerrsquos report on questions
54
3
35
4
45
5
55
Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Q9
Q10
Q11
Q12
Secu
rity
Sco
re
Human Game Theory
59
Field Test Against Adversaries Mock Attackers
Example from PROTECT
ldquoMock attackerrdquo team deployed in Boston
Comparing PRE- to POST-PROTECT ldquodeterrencerdquo improved
Additional real-world indicators from Boston
Boston boaters questions
ldquohas the Coast Guard recently acquired more boatsrdquo
POST-PROTECT Actual reports of illegal activity
55
5956
Field Tests Against Adversaries
Computational Game Theory in the Field
Game theory vs Random
21 days of patrol
Identical conditions
Random + Human
Not controlled
0
20
40
60
80
100 Miscellaneous
Drugs
Firearm Violations
0
5
10
15
20
Captures30 min
Warnings30 min
Violations30 min
Game Theory
Rand+Human
Controlled
59
Expert Evaluation
Example from ARMOR IRIS amp PROTECT
February 2009 Commendations
LAX Police (City of Los Angeles)
July 2011 Operational Excellence
Award (US Coast Guard Boston)
September 2011 Certificate of
Appreciation (Federal Air Marshals)
June 2013 Meritorious Team Commendation
from Commandant (US Coast Guard)
57
59
Summary Security Games
Decision aids based on computational game theory in daily use
Optimize limited security resources against adversaries
Applications yield research challenges Science of security games
Scale-up Incremental strategy generation amp Marginals
Uncertainty Integrate MDPs Robustness Quantal response
Current applications (wildlife security) Interdisciplinar challenge
Global challenges Merge planninglearning amp security games
58
59
Just the Beginning of ldquoSecurity Gamesrdquohellip
Our next steps
59
Thank you
to sponsors
StartupARMORWAY Game theory in the field
Follow on research amp applications
Privacy audits
(Sinha IJCAIrsquo13)
Software testing
(Kukreja ASErsquo13)
Sport event security
(Yin AAAIrsquo14)
Singapore train
(Varakantham IAAIrsquo13)
Exam questions
(Li IJCAIrsquo13)
59
THANK YOU
tambeuscedu
httpteamcoreuscedusecurity
60
59
IRIS Deployed FAMS (2009-)
ldquohellipin 2011 the Military Operations Research Society selected a University
of Southern California project with FAMS on randomizing flight
schedules for the prestigious Rist Awardhelliprdquo
-R S Bray (TSA)
Transportation Security Subcommittee
US House of Representatives 2012
19
Significant change in operations
59
Networks Mumbai Police Checkpoints[2013]
150 edges 2 Checkpoints
150-choose-2 strategies
With V Conitzer 20
Jain
59
Networks Mumbai Police Checkpoints[2013]
Incremental Strategy Generation
With V Conitzer 21
Jain
Double oracle Converge to a global optimal
Path 1 Path 2
Checkpoint
strategy 15 -5 -1 1
Path 1 Path 2
Checkpoint
strategy 15 -5 -1 1
Checkpoint
strategy 2-5 5 2 -1
Defender oracle
Path 1 Path 2 Path 3
Checkpoint
strategy 15 -5 -1 1 -2 2
Checkpoint
strategy 2-5 5 1 -1 -2 2
Attacker oracle
Path 1 Path 2 Path 3
Checkpoint
strategy 15 -5 -1 1 -2 2
Checkpoint
strategy 2-5 5 1 -1 -2 2
59
Double Oracle[2013]Incremental Strategy Generation Exploit Small Support
22
150 edges 2 Checkpoints
Only six candidate edges
for checkpoints
Jain
5923
Mumbai Police Checkpoints[2013]Results of Scale-up
20416 Roads15 checkpoints
20 min
59
Double Oracle Social Cyber Networks[2013]
Incremental Strategy Generation
24
Social networkseg counter-insurgency
Sources
Intermediate
Nodes
Links
Targets
Cyber networks
Tsai
59
Outline ldquoSecurity Gamesrdquo Research
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
25
59
Port Security Threat Scenarios
US Ports $315 trillion economy
USS Cole after suicide attack
French oil tanker hit by small boat
Attack on a ferry
26
59
PROTECT Randomized Patrol Scheduling [2013]
27
Port Protection (Scale-up) and Ferries (Continuous Spacetime)
59
PROTECT Randomized Patrol Scheduling [2013]Port Protection (Scale-up) and Ferries (Continuous Spacetime)
28
59
Ferries Scale-up with Mobile Resources amp Moving TargetsTransition Graph Representation
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
29
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Ferries Scale-up with Mobile Resources amp Moving TargetsTransition Graph Representation
30
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
31
Patrols protect nearby ferry location Solve as done in ARMOR
Ferries Patrol RoutesExponential Numbers of Patrol Routes
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Patrols protect nearby ferry location Solve as done in ARMOR
Ferries Patrol RoutesExponential Numbers of Patrol Routes
32
Pr([(B5) (C 10) (C15)]) = 017
Pr([(B5) (C10) (B15)]) =013
NT variables
Pr([(A5) (A10) (B15)]) = 007
Pr([(A5) (A10) (A15)]) = 003
Jiang KiekintveldFang
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Variables NOT routes but probability flow over each segment
Ferries Scale-up Marginal Probabilities Over Segments
33
NT variables
Jiang KiekintveldFang
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
030
017
013
Obtain marginal probabilities over segments
010
007
003
N2T variablesExtract Pr([(B5) (C 10) (C15)]) = 017
Pr([(B5) (C10) (B15)]) =013
Ferries Scale-up with Marginals Over Separable SegmentsSignificant Speedup
34
NT variables
59
5936
Outline ldquoSecurity Gamesrdquo Research (2007-Now)
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
Evaluation II Real-world deployments
(Patience)
Evaluation I Scale up Handle uncertainty
59
TRUSTS Frequent adversary interaction games
Patrols Against Fare Evaders
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
017
013
010
010
37
59
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
38
Jiang Delle Fave
59
Markov Decision Problems in Security games
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
39
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution Jiang Delle Fave
5940
Urban Transportation Security
MDPs amp DEC-MDPs in Security Games
COPS LA Metro System(Against Opportunistic Crime)
STREETS Singapore Roads(Against Reckless Driving)
DEC-MDPs in Security Games Teamwork + Uncertainty
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
ZhangShieh
59
Uncertainty Space Algorithms
Bayesian and Robust Approaches
Adversary payoff uncertainty
Adversary observation amp
defender execution
uncertainty
Adversary rationality
uncertainty
RECON
BRASS
Monotonic Maximin
(Monotonic adversary)
41
URAC
Payoff interval
Not point
estimateGMC HUNTER
Bayesian
Robust
Yin Nguyen An
0
05
1
15
5 10
Def
end
ers
EU
Targets
ISG RECON URAC
59
Security Games Environmental Crime
amp Bounded Rationality
FisheryGulf of Mexico
Nakai Nam Theun
Forest Area Laos
WildlifeQueen Elizabeth National
ParkUganda
No patrols
Higher
density
Lower densityx
Yang Ford Brown
59
Uncertainty in Adversary Decision Bounded Rationality
Human Subjects as Poachers
59
Uncertainty in Adversary Decision[2009]
Human subjects Anchoring e-Optimality
-4
-3
-2
-1
0
1
Unobserved 5 Observations 20 Observations Unlimited
Average expected reward
DOBSS
Uniform
COBRA
ARMOR
ARMOR Outperforms uniform random
COBRA
MqxCaq
Xxxts
qxR
ji
Xi
ijj
Xi Qj
jiijqx
)1()()1(
|)|1()1(
max
ee
Anchoring
e-optimality
With Sarit Kraus 44
Pita
5945
Quantal Response Stochastic choice better choice more likely
Quantal Response Model of Adversary [2011] Not Maximize Expected Utility
T
j
jxadversary
EU
jxadversary
EU
j
e
eq
1
))((
))((
-3
-25
-2
-15
-1
-05
0
05
1
15
2
Payoff 1 Payoff 2 Payoff 3 Payoff 4
QuantalResponse
COBRA
ARMOR
Adversaryrsquos probability
of choosing target j
Yang
10
)(max1
1
)(
)(
t tt
defendT
jT
j
jxEU
jxEU
x
xKxts
jxEU
e
e
adversary
adversary
59
Uncertainty in Adversary Decision [2012]
Robust vs Modeling Adversaries
Robustness Bound loss to defender Not model attacker via QR
Defeating Robust Learned subjective utility
Robust wins Draw QR wins
42 52 6
Results on 100 games
SU-QR wins Draw Robust
13 8 1
Results on 22 games
MqxCa
qxR
ji
Xi
ij
jiij
Xi Qj
qx
)1()(0
max
β (Adversaryrsquos utility loss if
deviates from optimal) gt=
(Defenderrsquos utility loss due to
adversary deviation)
penaltyattackw
rewardattackw
probcapturewjaSEU
3
2
1)(
M
j
jxSEU
jxSEU
jadversary
adversary
e
eq
1
)(
)(
SU-QR wins Draw Robust
6 13 3
Results against security experts
With Sarit Kraus 46
5947
PAWS Protection Assistant for Wildlife Security[2014]Repeated Stackelberg Game
Simulation
Learn from crime data
Improve model
Defender calculates
mixed strategy
Defender executes
randomized patrols
Poachers attack targets
119890119878119864119880119894(w1w2w3)
119896 119890119878119864119880119896(w1w2w3)
Bayesian SUQR Heterogeneous Poachers
5948
PAWS Test April 2014
Trials in Queen Elizabeth National Park
Andrew Lemieux with rangers
on PAWS patrol in Uganda
59
Security Resource OptimizationEvaluating Deployed Security Systems Not Easy
Game theory Improvement over previous approaches
Previous Human schedulers or ldquosimple randomrdquo
49
Lab
Evaluation
Simulated
adversary
Human subject
adversaries
Field Evaluation
Patrol quality
Unpredictable Cover
Compare real schedules
Scheduling competition
Expert evaluation
Field Evaluation
Tests against adversaries
ldquoMock attackersrdquo
Capture rates of
real adversaries
59
Why Does Game Theory Perform Better
Weaknesses of Previous Methods
Human schedulers
Predictable patterns eg US Coast Guard
Scheduling effort amp cognitive burden
Simple random (eg dice roll)
Wrong weightscoverage eg officers to sparsely crowded terminals
No adversary reactions
Multiple deployments over multiple years without us forcing them
50
59
Lab Evaluation via Simulations
Example from IRIS (FAMS)
-10
-8
-6
-4
-2
0
2
4
6
50 150 250
Defe
nde
r E
xpe
cte
d u
tilit
y
Schedule Size
Uniform Weighted random 1 Weighted random 2 IRIS
51
59
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage
Patrols Before PROTECT Boston Patrols After PROTECT Boston
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Base Patrol Area
52
PROTECT (Coast Guard) 350 increase defender expected utility
59
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage for Less Effort
53
IRIS for FAMS Outperformed expert human over six monthsReportGAO-09-903T
ARMOR at LAX Savings of up to an hour a day in scheduling
59
Field Evaluation Human vs Game Theory Competition
Counter-terrorism Patrol Scheduling
90 officers on LA Metro Trains
Humans required significant effort
Worse schedules than game theory
Observerrsquos report on questions
54
3
35
4
45
5
55
Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Q9
Q10
Q11
Q12
Secu
rity
Sco
re
Human Game Theory
59
Field Test Against Adversaries Mock Attackers
Example from PROTECT
ldquoMock attackerrdquo team deployed in Boston
Comparing PRE- to POST-PROTECT ldquodeterrencerdquo improved
Additional real-world indicators from Boston
Boston boaters questions
ldquohas the Coast Guard recently acquired more boatsrdquo
POST-PROTECT Actual reports of illegal activity
55
5956
Field Tests Against Adversaries
Computational Game Theory in the Field
Game theory vs Random
21 days of patrol
Identical conditions
Random + Human
Not controlled
0
20
40
60
80
100 Miscellaneous
Drugs
Firearm Violations
0
5
10
15
20
Captures30 min
Warnings30 min
Violations30 min
Game Theory
Rand+Human
Controlled
59
Expert Evaluation
Example from ARMOR IRIS amp PROTECT
February 2009 Commendations
LAX Police (City of Los Angeles)
July 2011 Operational Excellence
Award (US Coast Guard Boston)
September 2011 Certificate of
Appreciation (Federal Air Marshals)
June 2013 Meritorious Team Commendation
from Commandant (US Coast Guard)
57
59
Summary Security Games
Decision aids based on computational game theory in daily use
Optimize limited security resources against adversaries
Applications yield research challenges Science of security games
Scale-up Incremental strategy generation amp Marginals
Uncertainty Integrate MDPs Robustness Quantal response
Current applications (wildlife security) Interdisciplinar challenge
Global challenges Merge planninglearning amp security games
58
59
Just the Beginning of ldquoSecurity Gamesrdquohellip
Our next steps
59
Thank you
to sponsors
StartupARMORWAY Game theory in the field
Follow on research amp applications
Privacy audits
(Sinha IJCAIrsquo13)
Software testing
(Kukreja ASErsquo13)
Sport event security
(Yin AAAIrsquo14)
Singapore train
(Varakantham IAAIrsquo13)
Exam questions
(Li IJCAIrsquo13)
59
THANK YOU
tambeuscedu
httpteamcoreuscedusecurity
60
59
Networks Mumbai Police Checkpoints[2013]
150 edges 2 Checkpoints
150-choose-2 strategies
With V Conitzer 20
Jain
59
Networks Mumbai Police Checkpoints[2013]
Incremental Strategy Generation
With V Conitzer 21
Jain
Double oracle Converge to a global optimal
Path 1 Path 2
Checkpoint
strategy 15 -5 -1 1
Path 1 Path 2
Checkpoint
strategy 15 -5 -1 1
Checkpoint
strategy 2-5 5 2 -1
Defender oracle
Path 1 Path 2 Path 3
Checkpoint
strategy 15 -5 -1 1 -2 2
Checkpoint
strategy 2-5 5 1 -1 -2 2
Attacker oracle
Path 1 Path 2 Path 3
Checkpoint
strategy 15 -5 -1 1 -2 2
Checkpoint
strategy 2-5 5 1 -1 -2 2
59
Double Oracle[2013]Incremental Strategy Generation Exploit Small Support
22
150 edges 2 Checkpoints
Only six candidate edges
for checkpoints
Jain
5923
Mumbai Police Checkpoints[2013]Results of Scale-up
20416 Roads15 checkpoints
20 min
59
Double Oracle Social Cyber Networks[2013]
Incremental Strategy Generation
24
Social networkseg counter-insurgency
Sources
Intermediate
Nodes
Links
Targets
Cyber networks
Tsai
59
Outline ldquoSecurity Gamesrdquo Research
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
25
59
Port Security Threat Scenarios
US Ports $315 trillion economy
USS Cole after suicide attack
French oil tanker hit by small boat
Attack on a ferry
26
59
PROTECT Randomized Patrol Scheduling [2013]
27
Port Protection (Scale-up) and Ferries (Continuous Spacetime)
59
PROTECT Randomized Patrol Scheduling [2013]Port Protection (Scale-up) and Ferries (Continuous Spacetime)
28
59
Ferries Scale-up with Mobile Resources amp Moving TargetsTransition Graph Representation
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
29
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Ferries Scale-up with Mobile Resources amp Moving TargetsTransition Graph Representation
30
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
31
Patrols protect nearby ferry location Solve as done in ARMOR
Ferries Patrol RoutesExponential Numbers of Patrol Routes
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Patrols protect nearby ferry location Solve as done in ARMOR
Ferries Patrol RoutesExponential Numbers of Patrol Routes
32
Pr([(B5) (C 10) (C15)]) = 017
Pr([(B5) (C10) (B15)]) =013
NT variables
Pr([(A5) (A10) (B15)]) = 007
Pr([(A5) (A10) (A15)]) = 003
Jiang KiekintveldFang
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Variables NOT routes but probability flow over each segment
Ferries Scale-up Marginal Probabilities Over Segments
33
NT variables
Jiang KiekintveldFang
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
030
017
013
Obtain marginal probabilities over segments
010
007
003
N2T variablesExtract Pr([(B5) (C 10) (C15)]) = 017
Pr([(B5) (C10) (B15)]) =013
Ferries Scale-up with Marginals Over Separable SegmentsSignificant Speedup
34
NT variables
59
5936
Outline ldquoSecurity Gamesrdquo Research (2007-Now)
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
Evaluation II Real-world deployments
(Patience)
Evaluation I Scale up Handle uncertainty
59
TRUSTS Frequent adversary interaction games
Patrols Against Fare Evaders
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
017
013
010
010
37
59
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
38
Jiang Delle Fave
59
Markov Decision Problems in Security games
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
39
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution Jiang Delle Fave
5940
Urban Transportation Security
MDPs amp DEC-MDPs in Security Games
COPS LA Metro System(Against Opportunistic Crime)
STREETS Singapore Roads(Against Reckless Driving)
DEC-MDPs in Security Games Teamwork + Uncertainty
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
ZhangShieh
59
Uncertainty Space Algorithms
Bayesian and Robust Approaches
Adversary payoff uncertainty
Adversary observation amp
defender execution
uncertainty
Adversary rationality
uncertainty
RECON
BRASS
Monotonic Maximin
(Monotonic adversary)
41
URAC
Payoff interval
Not point
estimateGMC HUNTER
Bayesian
Robust
Yin Nguyen An
0
05
1
15
5 10
Def
end
ers
EU
Targets
ISG RECON URAC
59
Security Games Environmental Crime
amp Bounded Rationality
FisheryGulf of Mexico
Nakai Nam Theun
Forest Area Laos
WildlifeQueen Elizabeth National
ParkUganda
No patrols
Higher
density
Lower densityx
Yang Ford Brown
59
Uncertainty in Adversary Decision Bounded Rationality
Human Subjects as Poachers
59
Uncertainty in Adversary Decision[2009]
Human subjects Anchoring e-Optimality
-4
-3
-2
-1
0
1
Unobserved 5 Observations 20 Observations Unlimited
Average expected reward
DOBSS
Uniform
COBRA
ARMOR
ARMOR Outperforms uniform random
COBRA
MqxCaq
Xxxts
qxR
ji
Xi
ijj
Xi Qj
jiijqx
)1()()1(
|)|1()1(
max
ee
Anchoring
e-optimality
With Sarit Kraus 44
Pita
5945
Quantal Response Stochastic choice better choice more likely
Quantal Response Model of Adversary [2011] Not Maximize Expected Utility
T
j
jxadversary
EU
jxadversary
EU
j
e
eq
1
))((
))((
-3
-25
-2
-15
-1
-05
0
05
1
15
2
Payoff 1 Payoff 2 Payoff 3 Payoff 4
QuantalResponse
COBRA
ARMOR
Adversaryrsquos probability
of choosing target j
Yang
10
)(max1
1
)(
)(
t tt
defendT
jT
j
jxEU
jxEU
x
xKxts
jxEU
e
e
adversary
adversary
59
Uncertainty in Adversary Decision [2012]
Robust vs Modeling Adversaries
Robustness Bound loss to defender Not model attacker via QR
Defeating Robust Learned subjective utility
Robust wins Draw QR wins
42 52 6
Results on 100 games
SU-QR wins Draw Robust
13 8 1
Results on 22 games
MqxCa
qxR
ji
Xi
ij
jiij
Xi Qj
qx
)1()(0
max
β (Adversaryrsquos utility loss if
deviates from optimal) gt=
(Defenderrsquos utility loss due to
adversary deviation)
penaltyattackw
rewardattackw
probcapturewjaSEU
3
2
1)(
M
j
jxSEU
jxSEU
jadversary
adversary
e
eq
1
)(
)(
SU-QR wins Draw Robust
6 13 3
Results against security experts
With Sarit Kraus 46
5947
PAWS Protection Assistant for Wildlife Security[2014]Repeated Stackelberg Game
Simulation
Learn from crime data
Improve model
Defender calculates
mixed strategy
Defender executes
randomized patrols
Poachers attack targets
119890119878119864119880119894(w1w2w3)
119896 119890119878119864119880119896(w1w2w3)
Bayesian SUQR Heterogeneous Poachers
5948
PAWS Test April 2014
Trials in Queen Elizabeth National Park
Andrew Lemieux with rangers
on PAWS patrol in Uganda
59
Security Resource OptimizationEvaluating Deployed Security Systems Not Easy
Game theory Improvement over previous approaches
Previous Human schedulers or ldquosimple randomrdquo
49
Lab
Evaluation
Simulated
adversary
Human subject
adversaries
Field Evaluation
Patrol quality
Unpredictable Cover
Compare real schedules
Scheduling competition
Expert evaluation
Field Evaluation
Tests against adversaries
ldquoMock attackersrdquo
Capture rates of
real adversaries
59
Why Does Game Theory Perform Better
Weaknesses of Previous Methods
Human schedulers
Predictable patterns eg US Coast Guard
Scheduling effort amp cognitive burden
Simple random (eg dice roll)
Wrong weightscoverage eg officers to sparsely crowded terminals
No adversary reactions
Multiple deployments over multiple years without us forcing them
50
59
Lab Evaluation via Simulations
Example from IRIS (FAMS)
-10
-8
-6
-4
-2
0
2
4
6
50 150 250
Defe
nde
r E
xpe
cte
d u
tilit
y
Schedule Size
Uniform Weighted random 1 Weighted random 2 IRIS
51
59
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage
Patrols Before PROTECT Boston Patrols After PROTECT Boston
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Base Patrol Area
52
PROTECT (Coast Guard) 350 increase defender expected utility
59
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage for Less Effort
53
IRIS for FAMS Outperformed expert human over six monthsReportGAO-09-903T
ARMOR at LAX Savings of up to an hour a day in scheduling
59
Field Evaluation Human vs Game Theory Competition
Counter-terrorism Patrol Scheduling
90 officers on LA Metro Trains
Humans required significant effort
Worse schedules than game theory
Observerrsquos report on questions
54
3
35
4
45
5
55
Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Q9
Q10
Q11
Q12
Secu
rity
Sco
re
Human Game Theory
59
Field Test Against Adversaries Mock Attackers
Example from PROTECT
ldquoMock attackerrdquo team deployed in Boston
Comparing PRE- to POST-PROTECT ldquodeterrencerdquo improved
Additional real-world indicators from Boston
Boston boaters questions
ldquohas the Coast Guard recently acquired more boatsrdquo
POST-PROTECT Actual reports of illegal activity
55
5956
Field Tests Against Adversaries
Computational Game Theory in the Field
Game theory vs Random
21 days of patrol
Identical conditions
Random + Human
Not controlled
0
20
40
60
80
100 Miscellaneous
Drugs
Firearm Violations
0
5
10
15
20
Captures30 min
Warnings30 min
Violations30 min
Game Theory
Rand+Human
Controlled
59
Expert Evaluation
Example from ARMOR IRIS amp PROTECT
February 2009 Commendations
LAX Police (City of Los Angeles)
July 2011 Operational Excellence
Award (US Coast Guard Boston)
September 2011 Certificate of
Appreciation (Federal Air Marshals)
June 2013 Meritorious Team Commendation
from Commandant (US Coast Guard)
57
59
Summary Security Games
Decision aids based on computational game theory in daily use
Optimize limited security resources against adversaries
Applications yield research challenges Science of security games
Scale-up Incremental strategy generation amp Marginals
Uncertainty Integrate MDPs Robustness Quantal response
Current applications (wildlife security) Interdisciplinar challenge
Global challenges Merge planninglearning amp security games
58
59
Just the Beginning of ldquoSecurity Gamesrdquohellip
Our next steps
59
Thank you
to sponsors
StartupARMORWAY Game theory in the field
Follow on research amp applications
Privacy audits
(Sinha IJCAIrsquo13)
Software testing
(Kukreja ASErsquo13)
Sport event security
(Yin AAAIrsquo14)
Singapore train
(Varakantham IAAIrsquo13)
Exam questions
(Li IJCAIrsquo13)
59
THANK YOU
tambeuscedu
httpteamcoreuscedusecurity
60
59
Networks Mumbai Police Checkpoints[2013]
Incremental Strategy Generation
With V Conitzer 21
Jain
Double oracle Converge to a global optimal
Path 1 Path 2
Checkpoint
strategy 15 -5 -1 1
Path 1 Path 2
Checkpoint
strategy 15 -5 -1 1
Checkpoint
strategy 2-5 5 2 -1
Defender oracle
Path 1 Path 2 Path 3
Checkpoint
strategy 15 -5 -1 1 -2 2
Checkpoint
strategy 2-5 5 1 -1 -2 2
Attacker oracle
Path 1 Path 2 Path 3
Checkpoint
strategy 15 -5 -1 1 -2 2
Checkpoint
strategy 2-5 5 1 -1 -2 2
59
Double Oracle[2013]Incremental Strategy Generation Exploit Small Support
22
150 edges 2 Checkpoints
Only six candidate edges
for checkpoints
Jain
5923
Mumbai Police Checkpoints[2013]Results of Scale-up
20416 Roads15 checkpoints
20 min
59
Double Oracle Social Cyber Networks[2013]
Incremental Strategy Generation
24
Social networkseg counter-insurgency
Sources
Intermediate
Nodes
Links
Targets
Cyber networks
Tsai
59
Outline ldquoSecurity Gamesrdquo Research
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
25
59
Port Security Threat Scenarios
US Ports $315 trillion economy
USS Cole after suicide attack
French oil tanker hit by small boat
Attack on a ferry
26
59
PROTECT Randomized Patrol Scheduling [2013]
27
Port Protection (Scale-up) and Ferries (Continuous Spacetime)
59
PROTECT Randomized Patrol Scheduling [2013]Port Protection (Scale-up) and Ferries (Continuous Spacetime)
28
59
Ferries Scale-up with Mobile Resources amp Moving TargetsTransition Graph Representation
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
29
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Ferries Scale-up with Mobile Resources amp Moving TargetsTransition Graph Representation
30
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
31
Patrols protect nearby ferry location Solve as done in ARMOR
Ferries Patrol RoutesExponential Numbers of Patrol Routes
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Patrols protect nearby ferry location Solve as done in ARMOR
Ferries Patrol RoutesExponential Numbers of Patrol Routes
32
Pr([(B5) (C 10) (C15)]) = 017
Pr([(B5) (C10) (B15)]) =013
NT variables
Pr([(A5) (A10) (B15)]) = 007
Pr([(A5) (A10) (A15)]) = 003
Jiang KiekintveldFang
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Variables NOT routes but probability flow over each segment
Ferries Scale-up Marginal Probabilities Over Segments
33
NT variables
Jiang KiekintveldFang
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
030
017
013
Obtain marginal probabilities over segments
010
007
003
N2T variablesExtract Pr([(B5) (C 10) (C15)]) = 017
Pr([(B5) (C10) (B15)]) =013
Ferries Scale-up with Marginals Over Separable SegmentsSignificant Speedup
34
NT variables
59
5936
Outline ldquoSecurity Gamesrdquo Research (2007-Now)
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
Evaluation II Real-world deployments
(Patience)
Evaluation I Scale up Handle uncertainty
59
TRUSTS Frequent adversary interaction games
Patrols Against Fare Evaders
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
017
013
010
010
37
59
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
38
Jiang Delle Fave
59
Markov Decision Problems in Security games
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
39
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution Jiang Delle Fave
5940
Urban Transportation Security
MDPs amp DEC-MDPs in Security Games
COPS LA Metro System(Against Opportunistic Crime)
STREETS Singapore Roads(Against Reckless Driving)
DEC-MDPs in Security Games Teamwork + Uncertainty
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
ZhangShieh
59
Uncertainty Space Algorithms
Bayesian and Robust Approaches
Adversary payoff uncertainty
Adversary observation amp
defender execution
uncertainty
Adversary rationality
uncertainty
RECON
BRASS
Monotonic Maximin
(Monotonic adversary)
41
URAC
Payoff interval
Not point
estimateGMC HUNTER
Bayesian
Robust
Yin Nguyen An
0
05
1
15
5 10
Def
end
ers
EU
Targets
ISG RECON URAC
59
Security Games Environmental Crime
amp Bounded Rationality
FisheryGulf of Mexico
Nakai Nam Theun
Forest Area Laos
WildlifeQueen Elizabeth National
ParkUganda
No patrols
Higher
density
Lower densityx
Yang Ford Brown
59
Uncertainty in Adversary Decision Bounded Rationality
Human Subjects as Poachers
59
Uncertainty in Adversary Decision[2009]
Human subjects Anchoring e-Optimality
-4
-3
-2
-1
0
1
Unobserved 5 Observations 20 Observations Unlimited
Average expected reward
DOBSS
Uniform
COBRA
ARMOR
ARMOR Outperforms uniform random
COBRA
MqxCaq
Xxxts
qxR
ji
Xi
ijj
Xi Qj
jiijqx
)1()()1(
|)|1()1(
max
ee
Anchoring
e-optimality
With Sarit Kraus 44
Pita
5945
Quantal Response Stochastic choice better choice more likely
Quantal Response Model of Adversary [2011] Not Maximize Expected Utility
T
j
jxadversary
EU
jxadversary
EU
j
e
eq
1
))((
))((
-3
-25
-2
-15
-1
-05
0
05
1
15
2
Payoff 1 Payoff 2 Payoff 3 Payoff 4
QuantalResponse
COBRA
ARMOR
Adversaryrsquos probability
of choosing target j
Yang
10
)(max1
1
)(
)(
t tt
defendT
jT
j
jxEU
jxEU
x
xKxts
jxEU
e
e
adversary
adversary
59
Uncertainty in Adversary Decision [2012]
Robust vs Modeling Adversaries
Robustness Bound loss to defender Not model attacker via QR
Defeating Robust Learned subjective utility
Robust wins Draw QR wins
42 52 6
Results on 100 games
SU-QR wins Draw Robust
13 8 1
Results on 22 games
MqxCa
qxR
ji
Xi
ij
jiij
Xi Qj
qx
)1()(0
max
β (Adversaryrsquos utility loss if
deviates from optimal) gt=
(Defenderrsquos utility loss due to
adversary deviation)
penaltyattackw
rewardattackw
probcapturewjaSEU
3
2
1)(
M
j
jxSEU
jxSEU
jadversary
adversary
e
eq
1
)(
)(
SU-QR wins Draw Robust
6 13 3
Results against security experts
With Sarit Kraus 46
5947
PAWS Protection Assistant for Wildlife Security[2014]Repeated Stackelberg Game
Simulation
Learn from crime data
Improve model
Defender calculates
mixed strategy
Defender executes
randomized patrols
Poachers attack targets
119890119878119864119880119894(w1w2w3)
119896 119890119878119864119880119896(w1w2w3)
Bayesian SUQR Heterogeneous Poachers
5948
PAWS Test April 2014
Trials in Queen Elizabeth National Park
Andrew Lemieux with rangers
on PAWS patrol in Uganda
59
Security Resource OptimizationEvaluating Deployed Security Systems Not Easy
Game theory Improvement over previous approaches
Previous Human schedulers or ldquosimple randomrdquo
49
Lab
Evaluation
Simulated
adversary
Human subject
adversaries
Field Evaluation
Patrol quality
Unpredictable Cover
Compare real schedules
Scheduling competition
Expert evaluation
Field Evaluation
Tests against adversaries
ldquoMock attackersrdquo
Capture rates of
real adversaries
59
Why Does Game Theory Perform Better
Weaknesses of Previous Methods
Human schedulers
Predictable patterns eg US Coast Guard
Scheduling effort amp cognitive burden
Simple random (eg dice roll)
Wrong weightscoverage eg officers to sparsely crowded terminals
No adversary reactions
Multiple deployments over multiple years without us forcing them
50
59
Lab Evaluation via Simulations
Example from IRIS (FAMS)
-10
-8
-6
-4
-2
0
2
4
6
50 150 250
Defe
nde
r E
xpe
cte
d u
tilit
y
Schedule Size
Uniform Weighted random 1 Weighted random 2 IRIS
51
59
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage
Patrols Before PROTECT Boston Patrols After PROTECT Boston
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Base Patrol Area
52
PROTECT (Coast Guard) 350 increase defender expected utility
59
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage for Less Effort
53
IRIS for FAMS Outperformed expert human over six monthsReportGAO-09-903T
ARMOR at LAX Savings of up to an hour a day in scheduling
59
Field Evaluation Human vs Game Theory Competition
Counter-terrorism Patrol Scheduling
90 officers on LA Metro Trains
Humans required significant effort
Worse schedules than game theory
Observerrsquos report on questions
54
3
35
4
45
5
55
Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Q9
Q10
Q11
Q12
Secu
rity
Sco
re
Human Game Theory
59
Field Test Against Adversaries Mock Attackers
Example from PROTECT
ldquoMock attackerrdquo team deployed in Boston
Comparing PRE- to POST-PROTECT ldquodeterrencerdquo improved
Additional real-world indicators from Boston
Boston boaters questions
ldquohas the Coast Guard recently acquired more boatsrdquo
POST-PROTECT Actual reports of illegal activity
55
5956
Field Tests Against Adversaries
Computational Game Theory in the Field
Game theory vs Random
21 days of patrol
Identical conditions
Random + Human
Not controlled
0
20
40
60
80
100 Miscellaneous
Drugs
Firearm Violations
0
5
10
15
20
Captures30 min
Warnings30 min
Violations30 min
Game Theory
Rand+Human
Controlled
59
Expert Evaluation
Example from ARMOR IRIS amp PROTECT
February 2009 Commendations
LAX Police (City of Los Angeles)
July 2011 Operational Excellence
Award (US Coast Guard Boston)
September 2011 Certificate of
Appreciation (Federal Air Marshals)
June 2013 Meritorious Team Commendation
from Commandant (US Coast Guard)
57
59
Summary Security Games
Decision aids based on computational game theory in daily use
Optimize limited security resources against adversaries
Applications yield research challenges Science of security games
Scale-up Incremental strategy generation amp Marginals
Uncertainty Integrate MDPs Robustness Quantal response
Current applications (wildlife security) Interdisciplinar challenge
Global challenges Merge planninglearning amp security games
58
59
Just the Beginning of ldquoSecurity Gamesrdquohellip
Our next steps
59
Thank you
to sponsors
StartupARMORWAY Game theory in the field
Follow on research amp applications
Privacy audits
(Sinha IJCAIrsquo13)
Software testing
(Kukreja ASErsquo13)
Sport event security
(Yin AAAIrsquo14)
Singapore train
(Varakantham IAAIrsquo13)
Exam questions
(Li IJCAIrsquo13)
59
THANK YOU
tambeuscedu
httpteamcoreuscedusecurity
60
59
Double Oracle[2013]Incremental Strategy Generation Exploit Small Support
22
150 edges 2 Checkpoints
Only six candidate edges
for checkpoints
Jain
5923
Mumbai Police Checkpoints[2013]Results of Scale-up
20416 Roads15 checkpoints
20 min
59
Double Oracle Social Cyber Networks[2013]
Incremental Strategy Generation
24
Social networkseg counter-insurgency
Sources
Intermediate
Nodes
Links
Targets
Cyber networks
Tsai
59
Outline ldquoSecurity Gamesrdquo Research
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
25
59
Port Security Threat Scenarios
US Ports $315 trillion economy
USS Cole after suicide attack
French oil tanker hit by small boat
Attack on a ferry
26
59
PROTECT Randomized Patrol Scheduling [2013]
27
Port Protection (Scale-up) and Ferries (Continuous Spacetime)
59
PROTECT Randomized Patrol Scheduling [2013]Port Protection (Scale-up) and Ferries (Continuous Spacetime)
28
59
Ferries Scale-up with Mobile Resources amp Moving TargetsTransition Graph Representation
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
29
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Ferries Scale-up with Mobile Resources amp Moving TargetsTransition Graph Representation
30
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
31
Patrols protect nearby ferry location Solve as done in ARMOR
Ferries Patrol RoutesExponential Numbers of Patrol Routes
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Patrols protect nearby ferry location Solve as done in ARMOR
Ferries Patrol RoutesExponential Numbers of Patrol Routes
32
Pr([(B5) (C 10) (C15)]) = 017
Pr([(B5) (C10) (B15)]) =013
NT variables
Pr([(A5) (A10) (B15)]) = 007
Pr([(A5) (A10) (A15)]) = 003
Jiang KiekintveldFang
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Variables NOT routes but probability flow over each segment
Ferries Scale-up Marginal Probabilities Over Segments
33
NT variables
Jiang KiekintveldFang
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
030
017
013
Obtain marginal probabilities over segments
010
007
003
N2T variablesExtract Pr([(B5) (C 10) (C15)]) = 017
Pr([(B5) (C10) (B15)]) =013
Ferries Scale-up with Marginals Over Separable SegmentsSignificant Speedup
34
NT variables
59
5936
Outline ldquoSecurity Gamesrdquo Research (2007-Now)
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
Evaluation II Real-world deployments
(Patience)
Evaluation I Scale up Handle uncertainty
59
TRUSTS Frequent adversary interaction games
Patrols Against Fare Evaders
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
017
013
010
010
37
59
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
38
Jiang Delle Fave
59
Markov Decision Problems in Security games
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
39
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution Jiang Delle Fave
5940
Urban Transportation Security
MDPs amp DEC-MDPs in Security Games
COPS LA Metro System(Against Opportunistic Crime)
STREETS Singapore Roads(Against Reckless Driving)
DEC-MDPs in Security Games Teamwork + Uncertainty
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
ZhangShieh
59
Uncertainty Space Algorithms
Bayesian and Robust Approaches
Adversary payoff uncertainty
Adversary observation amp
defender execution
uncertainty
Adversary rationality
uncertainty
RECON
BRASS
Monotonic Maximin
(Monotonic adversary)
41
URAC
Payoff interval
Not point
estimateGMC HUNTER
Bayesian
Robust
Yin Nguyen An
0
05
1
15
5 10
Def
end
ers
EU
Targets
ISG RECON URAC
59
Security Games Environmental Crime
amp Bounded Rationality
FisheryGulf of Mexico
Nakai Nam Theun
Forest Area Laos
WildlifeQueen Elizabeth National
ParkUganda
No patrols
Higher
density
Lower densityx
Yang Ford Brown
59
Uncertainty in Adversary Decision Bounded Rationality
Human Subjects as Poachers
59
Uncertainty in Adversary Decision[2009]
Human subjects Anchoring e-Optimality
-4
-3
-2
-1
0
1
Unobserved 5 Observations 20 Observations Unlimited
Average expected reward
DOBSS
Uniform
COBRA
ARMOR
ARMOR Outperforms uniform random
COBRA
MqxCaq
Xxxts
qxR
ji
Xi
ijj
Xi Qj
jiijqx
)1()()1(
|)|1()1(
max
ee
Anchoring
e-optimality
With Sarit Kraus 44
Pita
5945
Quantal Response Stochastic choice better choice more likely
Quantal Response Model of Adversary [2011] Not Maximize Expected Utility
T
j
jxadversary
EU
jxadversary
EU
j
e
eq
1
))((
))((
-3
-25
-2
-15
-1
-05
0
05
1
15
2
Payoff 1 Payoff 2 Payoff 3 Payoff 4
QuantalResponse
COBRA
ARMOR
Adversaryrsquos probability
of choosing target j
Yang
10
)(max1
1
)(
)(
t tt
defendT
jT
j
jxEU
jxEU
x
xKxts
jxEU
e
e
adversary
adversary
59
Uncertainty in Adversary Decision [2012]
Robust vs Modeling Adversaries
Robustness Bound loss to defender Not model attacker via QR
Defeating Robust Learned subjective utility
Robust wins Draw QR wins
42 52 6
Results on 100 games
SU-QR wins Draw Robust
13 8 1
Results on 22 games
MqxCa
qxR
ji
Xi
ij
jiij
Xi Qj
qx
)1()(0
max
β (Adversaryrsquos utility loss if
deviates from optimal) gt=
(Defenderrsquos utility loss due to
adversary deviation)
penaltyattackw
rewardattackw
probcapturewjaSEU
3
2
1)(
M
j
jxSEU
jxSEU
jadversary
adversary
e
eq
1
)(
)(
SU-QR wins Draw Robust
6 13 3
Results against security experts
With Sarit Kraus 46
5947
PAWS Protection Assistant for Wildlife Security[2014]Repeated Stackelberg Game
Simulation
Learn from crime data
Improve model
Defender calculates
mixed strategy
Defender executes
randomized patrols
Poachers attack targets
119890119878119864119880119894(w1w2w3)
119896 119890119878119864119880119896(w1w2w3)
Bayesian SUQR Heterogeneous Poachers
5948
PAWS Test April 2014
Trials in Queen Elizabeth National Park
Andrew Lemieux with rangers
on PAWS patrol in Uganda
59
Security Resource OptimizationEvaluating Deployed Security Systems Not Easy
Game theory Improvement over previous approaches
Previous Human schedulers or ldquosimple randomrdquo
49
Lab
Evaluation
Simulated
adversary
Human subject
adversaries
Field Evaluation
Patrol quality
Unpredictable Cover
Compare real schedules
Scheduling competition
Expert evaluation
Field Evaluation
Tests against adversaries
ldquoMock attackersrdquo
Capture rates of
real adversaries
59
Why Does Game Theory Perform Better
Weaknesses of Previous Methods
Human schedulers
Predictable patterns eg US Coast Guard
Scheduling effort amp cognitive burden
Simple random (eg dice roll)
Wrong weightscoverage eg officers to sparsely crowded terminals
No adversary reactions
Multiple deployments over multiple years without us forcing them
50
59
Lab Evaluation via Simulations
Example from IRIS (FAMS)
-10
-8
-6
-4
-2
0
2
4
6
50 150 250
Defe
nde
r E
xpe
cte
d u
tilit
y
Schedule Size
Uniform Weighted random 1 Weighted random 2 IRIS
51
59
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage
Patrols Before PROTECT Boston Patrols After PROTECT Boston
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Base Patrol Area
52
PROTECT (Coast Guard) 350 increase defender expected utility
59
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage for Less Effort
53
IRIS for FAMS Outperformed expert human over six monthsReportGAO-09-903T
ARMOR at LAX Savings of up to an hour a day in scheduling
59
Field Evaluation Human vs Game Theory Competition
Counter-terrorism Patrol Scheduling
90 officers on LA Metro Trains
Humans required significant effort
Worse schedules than game theory
Observerrsquos report on questions
54
3
35
4
45
5
55
Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Q9
Q10
Q11
Q12
Secu
rity
Sco
re
Human Game Theory
59
Field Test Against Adversaries Mock Attackers
Example from PROTECT
ldquoMock attackerrdquo team deployed in Boston
Comparing PRE- to POST-PROTECT ldquodeterrencerdquo improved
Additional real-world indicators from Boston
Boston boaters questions
ldquohas the Coast Guard recently acquired more boatsrdquo
POST-PROTECT Actual reports of illegal activity
55
5956
Field Tests Against Adversaries
Computational Game Theory in the Field
Game theory vs Random
21 days of patrol
Identical conditions
Random + Human
Not controlled
0
20
40
60
80
100 Miscellaneous
Drugs
Firearm Violations
0
5
10
15
20
Captures30 min
Warnings30 min
Violations30 min
Game Theory
Rand+Human
Controlled
59
Expert Evaluation
Example from ARMOR IRIS amp PROTECT
February 2009 Commendations
LAX Police (City of Los Angeles)
July 2011 Operational Excellence
Award (US Coast Guard Boston)
September 2011 Certificate of
Appreciation (Federal Air Marshals)
June 2013 Meritorious Team Commendation
from Commandant (US Coast Guard)
57
59
Summary Security Games
Decision aids based on computational game theory in daily use
Optimize limited security resources against adversaries
Applications yield research challenges Science of security games
Scale-up Incremental strategy generation amp Marginals
Uncertainty Integrate MDPs Robustness Quantal response
Current applications (wildlife security) Interdisciplinar challenge
Global challenges Merge planninglearning amp security games
58
59
Just the Beginning of ldquoSecurity Gamesrdquohellip
Our next steps
59
Thank you
to sponsors
StartupARMORWAY Game theory in the field
Follow on research amp applications
Privacy audits
(Sinha IJCAIrsquo13)
Software testing
(Kukreja ASErsquo13)
Sport event security
(Yin AAAIrsquo14)
Singapore train
(Varakantham IAAIrsquo13)
Exam questions
(Li IJCAIrsquo13)
59
THANK YOU
tambeuscedu
httpteamcoreuscedusecurity
60
5923
Mumbai Police Checkpoints[2013]Results of Scale-up
20416 Roads15 checkpoints
20 min
59
Double Oracle Social Cyber Networks[2013]
Incremental Strategy Generation
24
Social networkseg counter-insurgency
Sources
Intermediate
Nodes
Links
Targets
Cyber networks
Tsai
59
Outline ldquoSecurity Gamesrdquo Research
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
25
59
Port Security Threat Scenarios
US Ports $315 trillion economy
USS Cole after suicide attack
French oil tanker hit by small boat
Attack on a ferry
26
59
PROTECT Randomized Patrol Scheduling [2013]
27
Port Protection (Scale-up) and Ferries (Continuous Spacetime)
59
PROTECT Randomized Patrol Scheduling [2013]Port Protection (Scale-up) and Ferries (Continuous Spacetime)
28
59
Ferries Scale-up with Mobile Resources amp Moving TargetsTransition Graph Representation
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
29
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Ferries Scale-up with Mobile Resources amp Moving TargetsTransition Graph Representation
30
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
31
Patrols protect nearby ferry location Solve as done in ARMOR
Ferries Patrol RoutesExponential Numbers of Patrol Routes
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Patrols protect nearby ferry location Solve as done in ARMOR
Ferries Patrol RoutesExponential Numbers of Patrol Routes
32
Pr([(B5) (C 10) (C15)]) = 017
Pr([(B5) (C10) (B15)]) =013
NT variables
Pr([(A5) (A10) (B15)]) = 007
Pr([(A5) (A10) (A15)]) = 003
Jiang KiekintveldFang
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Variables NOT routes but probability flow over each segment
Ferries Scale-up Marginal Probabilities Over Segments
33
NT variables
Jiang KiekintveldFang
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
030
017
013
Obtain marginal probabilities over segments
010
007
003
N2T variablesExtract Pr([(B5) (C 10) (C15)]) = 017
Pr([(B5) (C10) (B15)]) =013
Ferries Scale-up with Marginals Over Separable SegmentsSignificant Speedup
34
NT variables
59
5936
Outline ldquoSecurity Gamesrdquo Research (2007-Now)
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
Evaluation II Real-world deployments
(Patience)
Evaluation I Scale up Handle uncertainty
59
TRUSTS Frequent adversary interaction games
Patrols Against Fare Evaders
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
017
013
010
010
37
59
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
38
Jiang Delle Fave
59
Markov Decision Problems in Security games
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
39
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution Jiang Delle Fave
5940
Urban Transportation Security
MDPs amp DEC-MDPs in Security Games
COPS LA Metro System(Against Opportunistic Crime)
STREETS Singapore Roads(Against Reckless Driving)
DEC-MDPs in Security Games Teamwork + Uncertainty
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
ZhangShieh
59
Uncertainty Space Algorithms
Bayesian and Robust Approaches
Adversary payoff uncertainty
Adversary observation amp
defender execution
uncertainty
Adversary rationality
uncertainty
RECON
BRASS
Monotonic Maximin
(Monotonic adversary)
41
URAC
Payoff interval
Not point
estimateGMC HUNTER
Bayesian
Robust
Yin Nguyen An
0
05
1
15
5 10
Def
end
ers
EU
Targets
ISG RECON URAC
59
Security Games Environmental Crime
amp Bounded Rationality
FisheryGulf of Mexico
Nakai Nam Theun
Forest Area Laos
WildlifeQueen Elizabeth National
ParkUganda
No patrols
Higher
density
Lower densityx
Yang Ford Brown
59
Uncertainty in Adversary Decision Bounded Rationality
Human Subjects as Poachers
59
Uncertainty in Adversary Decision[2009]
Human subjects Anchoring e-Optimality
-4
-3
-2
-1
0
1
Unobserved 5 Observations 20 Observations Unlimited
Average expected reward
DOBSS
Uniform
COBRA
ARMOR
ARMOR Outperforms uniform random
COBRA
MqxCaq
Xxxts
qxR
ji
Xi
ijj
Xi Qj
jiijqx
)1()()1(
|)|1()1(
max
ee
Anchoring
e-optimality
With Sarit Kraus 44
Pita
5945
Quantal Response Stochastic choice better choice more likely
Quantal Response Model of Adversary [2011] Not Maximize Expected Utility
T
j
jxadversary
EU
jxadversary
EU
j
e
eq
1
))((
))((
-3
-25
-2
-15
-1
-05
0
05
1
15
2
Payoff 1 Payoff 2 Payoff 3 Payoff 4
QuantalResponse
COBRA
ARMOR
Adversaryrsquos probability
of choosing target j
Yang
10
)(max1
1
)(
)(
t tt
defendT
jT
j
jxEU
jxEU
x
xKxts
jxEU
e
e
adversary
adversary
59
Uncertainty in Adversary Decision [2012]
Robust vs Modeling Adversaries
Robustness Bound loss to defender Not model attacker via QR
Defeating Robust Learned subjective utility
Robust wins Draw QR wins
42 52 6
Results on 100 games
SU-QR wins Draw Robust
13 8 1
Results on 22 games
MqxCa
qxR
ji
Xi
ij
jiij
Xi Qj
qx
)1()(0
max
β (Adversaryrsquos utility loss if
deviates from optimal) gt=
(Defenderrsquos utility loss due to
adversary deviation)
penaltyattackw
rewardattackw
probcapturewjaSEU
3
2
1)(
M
j
jxSEU
jxSEU
jadversary
adversary
e
eq
1
)(
)(
SU-QR wins Draw Robust
6 13 3
Results against security experts
With Sarit Kraus 46
5947
PAWS Protection Assistant for Wildlife Security[2014]Repeated Stackelberg Game
Simulation
Learn from crime data
Improve model
Defender calculates
mixed strategy
Defender executes
randomized patrols
Poachers attack targets
119890119878119864119880119894(w1w2w3)
119896 119890119878119864119880119896(w1w2w3)
Bayesian SUQR Heterogeneous Poachers
5948
PAWS Test April 2014
Trials in Queen Elizabeth National Park
Andrew Lemieux with rangers
on PAWS patrol in Uganda
59
Security Resource OptimizationEvaluating Deployed Security Systems Not Easy
Game theory Improvement over previous approaches
Previous Human schedulers or ldquosimple randomrdquo
49
Lab
Evaluation
Simulated
adversary
Human subject
adversaries
Field Evaluation
Patrol quality
Unpredictable Cover
Compare real schedules
Scheduling competition
Expert evaluation
Field Evaluation
Tests against adversaries
ldquoMock attackersrdquo
Capture rates of
real adversaries
59
Why Does Game Theory Perform Better
Weaknesses of Previous Methods
Human schedulers
Predictable patterns eg US Coast Guard
Scheduling effort amp cognitive burden
Simple random (eg dice roll)
Wrong weightscoverage eg officers to sparsely crowded terminals
No adversary reactions
Multiple deployments over multiple years without us forcing them
50
59
Lab Evaluation via Simulations
Example from IRIS (FAMS)
-10
-8
-6
-4
-2
0
2
4
6
50 150 250
Defe
nde
r E
xpe
cte
d u
tilit
y
Schedule Size
Uniform Weighted random 1 Weighted random 2 IRIS
51
59
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage
Patrols Before PROTECT Boston Patrols After PROTECT Boston
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Base Patrol Area
52
PROTECT (Coast Guard) 350 increase defender expected utility
59
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage for Less Effort
53
IRIS for FAMS Outperformed expert human over six monthsReportGAO-09-903T
ARMOR at LAX Savings of up to an hour a day in scheduling
59
Field Evaluation Human vs Game Theory Competition
Counter-terrorism Patrol Scheduling
90 officers on LA Metro Trains
Humans required significant effort
Worse schedules than game theory
Observerrsquos report on questions
54
3
35
4
45
5
55
Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Q9
Q10
Q11
Q12
Secu
rity
Sco
re
Human Game Theory
59
Field Test Against Adversaries Mock Attackers
Example from PROTECT
ldquoMock attackerrdquo team deployed in Boston
Comparing PRE- to POST-PROTECT ldquodeterrencerdquo improved
Additional real-world indicators from Boston
Boston boaters questions
ldquohas the Coast Guard recently acquired more boatsrdquo
POST-PROTECT Actual reports of illegal activity
55
5956
Field Tests Against Adversaries
Computational Game Theory in the Field
Game theory vs Random
21 days of patrol
Identical conditions
Random + Human
Not controlled
0
20
40
60
80
100 Miscellaneous
Drugs
Firearm Violations
0
5
10
15
20
Captures30 min
Warnings30 min
Violations30 min
Game Theory
Rand+Human
Controlled
59
Expert Evaluation
Example from ARMOR IRIS amp PROTECT
February 2009 Commendations
LAX Police (City of Los Angeles)
July 2011 Operational Excellence
Award (US Coast Guard Boston)
September 2011 Certificate of
Appreciation (Federal Air Marshals)
June 2013 Meritorious Team Commendation
from Commandant (US Coast Guard)
57
59
Summary Security Games
Decision aids based on computational game theory in daily use
Optimize limited security resources against adversaries
Applications yield research challenges Science of security games
Scale-up Incremental strategy generation amp Marginals
Uncertainty Integrate MDPs Robustness Quantal response
Current applications (wildlife security) Interdisciplinar challenge
Global challenges Merge planninglearning amp security games
58
59
Just the Beginning of ldquoSecurity Gamesrdquohellip
Our next steps
59
Thank you
to sponsors
StartupARMORWAY Game theory in the field
Follow on research amp applications
Privacy audits
(Sinha IJCAIrsquo13)
Software testing
(Kukreja ASErsquo13)
Sport event security
(Yin AAAIrsquo14)
Singapore train
(Varakantham IAAIrsquo13)
Exam questions
(Li IJCAIrsquo13)
59
THANK YOU
tambeuscedu
httpteamcoreuscedusecurity
60
59
Double Oracle Social Cyber Networks[2013]
Incremental Strategy Generation
24
Social networkseg counter-insurgency
Sources
Intermediate
Nodes
Links
Targets
Cyber networks
Tsai
59
Outline ldquoSecurity Gamesrdquo Research
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
25
59
Port Security Threat Scenarios
US Ports $315 trillion economy
USS Cole after suicide attack
French oil tanker hit by small boat
Attack on a ferry
26
59
PROTECT Randomized Patrol Scheduling [2013]
27
Port Protection (Scale-up) and Ferries (Continuous Spacetime)
59
PROTECT Randomized Patrol Scheduling [2013]Port Protection (Scale-up) and Ferries (Continuous Spacetime)
28
59
Ferries Scale-up with Mobile Resources amp Moving TargetsTransition Graph Representation
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
29
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Ferries Scale-up with Mobile Resources amp Moving TargetsTransition Graph Representation
30
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
31
Patrols protect nearby ferry location Solve as done in ARMOR
Ferries Patrol RoutesExponential Numbers of Patrol Routes
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Patrols protect nearby ferry location Solve as done in ARMOR
Ferries Patrol RoutesExponential Numbers of Patrol Routes
32
Pr([(B5) (C 10) (C15)]) = 017
Pr([(B5) (C10) (B15)]) =013
NT variables
Pr([(A5) (A10) (B15)]) = 007
Pr([(A5) (A10) (A15)]) = 003
Jiang KiekintveldFang
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Variables NOT routes but probability flow over each segment
Ferries Scale-up Marginal Probabilities Over Segments
33
NT variables
Jiang KiekintveldFang
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
030
017
013
Obtain marginal probabilities over segments
010
007
003
N2T variablesExtract Pr([(B5) (C 10) (C15)]) = 017
Pr([(B5) (C10) (B15)]) =013
Ferries Scale-up with Marginals Over Separable SegmentsSignificant Speedup
34
NT variables
59
5936
Outline ldquoSecurity Gamesrdquo Research (2007-Now)
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
Evaluation II Real-world deployments
(Patience)
Evaluation I Scale up Handle uncertainty
59
TRUSTS Frequent adversary interaction games
Patrols Against Fare Evaders
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
017
013
010
010
37
59
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
38
Jiang Delle Fave
59
Markov Decision Problems in Security games
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
39
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution Jiang Delle Fave
5940
Urban Transportation Security
MDPs amp DEC-MDPs in Security Games
COPS LA Metro System(Against Opportunistic Crime)
STREETS Singapore Roads(Against Reckless Driving)
DEC-MDPs in Security Games Teamwork + Uncertainty
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
ZhangShieh
59
Uncertainty Space Algorithms
Bayesian and Robust Approaches
Adversary payoff uncertainty
Adversary observation amp
defender execution
uncertainty
Adversary rationality
uncertainty
RECON
BRASS
Monotonic Maximin
(Monotonic adversary)
41
URAC
Payoff interval
Not point
estimateGMC HUNTER
Bayesian
Robust
Yin Nguyen An
0
05
1
15
5 10
Def
end
ers
EU
Targets
ISG RECON URAC
59
Security Games Environmental Crime
amp Bounded Rationality
FisheryGulf of Mexico
Nakai Nam Theun
Forest Area Laos
WildlifeQueen Elizabeth National
ParkUganda
No patrols
Higher
density
Lower densityx
Yang Ford Brown
59
Uncertainty in Adversary Decision Bounded Rationality
Human Subjects as Poachers
59
Uncertainty in Adversary Decision[2009]
Human subjects Anchoring e-Optimality
-4
-3
-2
-1
0
1
Unobserved 5 Observations 20 Observations Unlimited
Average expected reward
DOBSS
Uniform
COBRA
ARMOR
ARMOR Outperforms uniform random
COBRA
MqxCaq
Xxxts
qxR
ji
Xi
ijj
Xi Qj
jiijqx
)1()()1(
|)|1()1(
max
ee
Anchoring
e-optimality
With Sarit Kraus 44
Pita
5945
Quantal Response Stochastic choice better choice more likely
Quantal Response Model of Adversary [2011] Not Maximize Expected Utility
T
j
jxadversary
EU
jxadversary
EU
j
e
eq
1
))((
))((
-3
-25
-2
-15
-1
-05
0
05
1
15
2
Payoff 1 Payoff 2 Payoff 3 Payoff 4
QuantalResponse
COBRA
ARMOR
Adversaryrsquos probability
of choosing target j
Yang
10
)(max1
1
)(
)(
t tt
defendT
jT
j
jxEU
jxEU
x
xKxts
jxEU
e
e
adversary
adversary
59
Uncertainty in Adversary Decision [2012]
Robust vs Modeling Adversaries
Robustness Bound loss to defender Not model attacker via QR
Defeating Robust Learned subjective utility
Robust wins Draw QR wins
42 52 6
Results on 100 games
SU-QR wins Draw Robust
13 8 1
Results on 22 games
MqxCa
qxR
ji
Xi
ij
jiij
Xi Qj
qx
)1()(0
max
β (Adversaryrsquos utility loss if
deviates from optimal) gt=
(Defenderrsquos utility loss due to
adversary deviation)
penaltyattackw
rewardattackw
probcapturewjaSEU
3
2
1)(
M
j
jxSEU
jxSEU
jadversary
adversary
e
eq
1
)(
)(
SU-QR wins Draw Robust
6 13 3
Results against security experts
With Sarit Kraus 46
5947
PAWS Protection Assistant for Wildlife Security[2014]Repeated Stackelberg Game
Simulation
Learn from crime data
Improve model
Defender calculates
mixed strategy
Defender executes
randomized patrols
Poachers attack targets
119890119878119864119880119894(w1w2w3)
119896 119890119878119864119880119896(w1w2w3)
Bayesian SUQR Heterogeneous Poachers
5948
PAWS Test April 2014
Trials in Queen Elizabeth National Park
Andrew Lemieux with rangers
on PAWS patrol in Uganda
59
Security Resource OptimizationEvaluating Deployed Security Systems Not Easy
Game theory Improvement over previous approaches
Previous Human schedulers or ldquosimple randomrdquo
49
Lab
Evaluation
Simulated
adversary
Human subject
adversaries
Field Evaluation
Patrol quality
Unpredictable Cover
Compare real schedules
Scheduling competition
Expert evaluation
Field Evaluation
Tests against adversaries
ldquoMock attackersrdquo
Capture rates of
real adversaries
59
Why Does Game Theory Perform Better
Weaknesses of Previous Methods
Human schedulers
Predictable patterns eg US Coast Guard
Scheduling effort amp cognitive burden
Simple random (eg dice roll)
Wrong weightscoverage eg officers to sparsely crowded terminals
No adversary reactions
Multiple deployments over multiple years without us forcing them
50
59
Lab Evaluation via Simulations
Example from IRIS (FAMS)
-10
-8
-6
-4
-2
0
2
4
6
50 150 250
Defe
nde
r E
xpe
cte
d u
tilit
y
Schedule Size
Uniform Weighted random 1 Weighted random 2 IRIS
51
59
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage
Patrols Before PROTECT Boston Patrols After PROTECT Boston
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Base Patrol Area
52
PROTECT (Coast Guard) 350 increase defender expected utility
59
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage for Less Effort
53
IRIS for FAMS Outperformed expert human over six monthsReportGAO-09-903T
ARMOR at LAX Savings of up to an hour a day in scheduling
59
Field Evaluation Human vs Game Theory Competition
Counter-terrorism Patrol Scheduling
90 officers on LA Metro Trains
Humans required significant effort
Worse schedules than game theory
Observerrsquos report on questions
54
3
35
4
45
5
55
Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Q9
Q10
Q11
Q12
Secu
rity
Sco
re
Human Game Theory
59
Field Test Against Adversaries Mock Attackers
Example from PROTECT
ldquoMock attackerrdquo team deployed in Boston
Comparing PRE- to POST-PROTECT ldquodeterrencerdquo improved
Additional real-world indicators from Boston
Boston boaters questions
ldquohas the Coast Guard recently acquired more boatsrdquo
POST-PROTECT Actual reports of illegal activity
55
5956
Field Tests Against Adversaries
Computational Game Theory in the Field
Game theory vs Random
21 days of patrol
Identical conditions
Random + Human
Not controlled
0
20
40
60
80
100 Miscellaneous
Drugs
Firearm Violations
0
5
10
15
20
Captures30 min
Warnings30 min
Violations30 min
Game Theory
Rand+Human
Controlled
59
Expert Evaluation
Example from ARMOR IRIS amp PROTECT
February 2009 Commendations
LAX Police (City of Los Angeles)
July 2011 Operational Excellence
Award (US Coast Guard Boston)
September 2011 Certificate of
Appreciation (Federal Air Marshals)
June 2013 Meritorious Team Commendation
from Commandant (US Coast Guard)
57
59
Summary Security Games
Decision aids based on computational game theory in daily use
Optimize limited security resources against adversaries
Applications yield research challenges Science of security games
Scale-up Incremental strategy generation amp Marginals
Uncertainty Integrate MDPs Robustness Quantal response
Current applications (wildlife security) Interdisciplinar challenge
Global challenges Merge planninglearning amp security games
58
59
Just the Beginning of ldquoSecurity Gamesrdquohellip
Our next steps
59
Thank you
to sponsors
StartupARMORWAY Game theory in the field
Follow on research amp applications
Privacy audits
(Sinha IJCAIrsquo13)
Software testing
(Kukreja ASErsquo13)
Sport event security
(Yin AAAIrsquo14)
Singapore train
(Varakantham IAAIrsquo13)
Exam questions
(Li IJCAIrsquo13)
59
THANK YOU
tambeuscedu
httpteamcoreuscedusecurity
60
59
Outline ldquoSecurity Gamesrdquo Research
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
25
59
Port Security Threat Scenarios
US Ports $315 trillion economy
USS Cole after suicide attack
French oil tanker hit by small boat
Attack on a ferry
26
59
PROTECT Randomized Patrol Scheduling [2013]
27
Port Protection (Scale-up) and Ferries (Continuous Spacetime)
59
PROTECT Randomized Patrol Scheduling [2013]Port Protection (Scale-up) and Ferries (Continuous Spacetime)
28
59
Ferries Scale-up with Mobile Resources amp Moving TargetsTransition Graph Representation
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
29
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Ferries Scale-up with Mobile Resources amp Moving TargetsTransition Graph Representation
30
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
31
Patrols protect nearby ferry location Solve as done in ARMOR
Ferries Patrol RoutesExponential Numbers of Patrol Routes
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Patrols protect nearby ferry location Solve as done in ARMOR
Ferries Patrol RoutesExponential Numbers of Patrol Routes
32
Pr([(B5) (C 10) (C15)]) = 017
Pr([(B5) (C10) (B15)]) =013
NT variables
Pr([(A5) (A10) (B15)]) = 007
Pr([(A5) (A10) (A15)]) = 003
Jiang KiekintveldFang
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Variables NOT routes but probability flow over each segment
Ferries Scale-up Marginal Probabilities Over Segments
33
NT variables
Jiang KiekintveldFang
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
030
017
013
Obtain marginal probabilities over segments
010
007
003
N2T variablesExtract Pr([(B5) (C 10) (C15)]) = 017
Pr([(B5) (C10) (B15)]) =013
Ferries Scale-up with Marginals Over Separable SegmentsSignificant Speedup
34
NT variables
59
5936
Outline ldquoSecurity Gamesrdquo Research (2007-Now)
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
Evaluation II Real-world deployments
(Patience)
Evaluation I Scale up Handle uncertainty
59
TRUSTS Frequent adversary interaction games
Patrols Against Fare Evaders
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
017
013
010
010
37
59
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
38
Jiang Delle Fave
59
Markov Decision Problems in Security games
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
39
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution Jiang Delle Fave
5940
Urban Transportation Security
MDPs amp DEC-MDPs in Security Games
COPS LA Metro System(Against Opportunistic Crime)
STREETS Singapore Roads(Against Reckless Driving)
DEC-MDPs in Security Games Teamwork + Uncertainty
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
ZhangShieh
59
Uncertainty Space Algorithms
Bayesian and Robust Approaches
Adversary payoff uncertainty
Adversary observation amp
defender execution
uncertainty
Adversary rationality
uncertainty
RECON
BRASS
Monotonic Maximin
(Monotonic adversary)
41
URAC
Payoff interval
Not point
estimateGMC HUNTER
Bayesian
Robust
Yin Nguyen An
0
05
1
15
5 10
Def
end
ers
EU
Targets
ISG RECON URAC
59
Security Games Environmental Crime
amp Bounded Rationality
FisheryGulf of Mexico
Nakai Nam Theun
Forest Area Laos
WildlifeQueen Elizabeth National
ParkUganda
No patrols
Higher
density
Lower densityx
Yang Ford Brown
59
Uncertainty in Adversary Decision Bounded Rationality
Human Subjects as Poachers
59
Uncertainty in Adversary Decision[2009]
Human subjects Anchoring e-Optimality
-4
-3
-2
-1
0
1
Unobserved 5 Observations 20 Observations Unlimited
Average expected reward
DOBSS
Uniform
COBRA
ARMOR
ARMOR Outperforms uniform random
COBRA
MqxCaq
Xxxts
qxR
ji
Xi
ijj
Xi Qj
jiijqx
)1()()1(
|)|1()1(
max
ee
Anchoring
e-optimality
With Sarit Kraus 44
Pita
5945
Quantal Response Stochastic choice better choice more likely
Quantal Response Model of Adversary [2011] Not Maximize Expected Utility
T
j
jxadversary
EU
jxadversary
EU
j
e
eq
1
))((
))((
-3
-25
-2
-15
-1
-05
0
05
1
15
2
Payoff 1 Payoff 2 Payoff 3 Payoff 4
QuantalResponse
COBRA
ARMOR
Adversaryrsquos probability
of choosing target j
Yang
10
)(max1
1
)(
)(
t tt
defendT
jT
j
jxEU
jxEU
x
xKxts
jxEU
e
e
adversary
adversary
59
Uncertainty in Adversary Decision [2012]
Robust vs Modeling Adversaries
Robustness Bound loss to defender Not model attacker via QR
Defeating Robust Learned subjective utility
Robust wins Draw QR wins
42 52 6
Results on 100 games
SU-QR wins Draw Robust
13 8 1
Results on 22 games
MqxCa
qxR
ji
Xi
ij
jiij
Xi Qj
qx
)1()(0
max
β (Adversaryrsquos utility loss if
deviates from optimal) gt=
(Defenderrsquos utility loss due to
adversary deviation)
penaltyattackw
rewardattackw
probcapturewjaSEU
3
2
1)(
M
j
jxSEU
jxSEU
jadversary
adversary
e
eq
1
)(
)(
SU-QR wins Draw Robust
6 13 3
Results against security experts
With Sarit Kraus 46
5947
PAWS Protection Assistant for Wildlife Security[2014]Repeated Stackelberg Game
Simulation
Learn from crime data
Improve model
Defender calculates
mixed strategy
Defender executes
randomized patrols
Poachers attack targets
119890119878119864119880119894(w1w2w3)
119896 119890119878119864119880119896(w1w2w3)
Bayesian SUQR Heterogeneous Poachers
5948
PAWS Test April 2014
Trials in Queen Elizabeth National Park
Andrew Lemieux with rangers
on PAWS patrol in Uganda
59
Security Resource OptimizationEvaluating Deployed Security Systems Not Easy
Game theory Improvement over previous approaches
Previous Human schedulers or ldquosimple randomrdquo
49
Lab
Evaluation
Simulated
adversary
Human subject
adversaries
Field Evaluation
Patrol quality
Unpredictable Cover
Compare real schedules
Scheduling competition
Expert evaluation
Field Evaluation
Tests against adversaries
ldquoMock attackersrdquo
Capture rates of
real adversaries
59
Why Does Game Theory Perform Better
Weaknesses of Previous Methods
Human schedulers
Predictable patterns eg US Coast Guard
Scheduling effort amp cognitive burden
Simple random (eg dice roll)
Wrong weightscoverage eg officers to sparsely crowded terminals
No adversary reactions
Multiple deployments over multiple years without us forcing them
50
59
Lab Evaluation via Simulations
Example from IRIS (FAMS)
-10
-8
-6
-4
-2
0
2
4
6
50 150 250
Defe
nde
r E
xpe
cte
d u
tilit
y
Schedule Size
Uniform Weighted random 1 Weighted random 2 IRIS
51
59
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage
Patrols Before PROTECT Boston Patrols After PROTECT Boston
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Base Patrol Area
52
PROTECT (Coast Guard) 350 increase defender expected utility
59
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage for Less Effort
53
IRIS for FAMS Outperformed expert human over six monthsReportGAO-09-903T
ARMOR at LAX Savings of up to an hour a day in scheduling
59
Field Evaluation Human vs Game Theory Competition
Counter-terrorism Patrol Scheduling
90 officers on LA Metro Trains
Humans required significant effort
Worse schedules than game theory
Observerrsquos report on questions
54
3
35
4
45
5
55
Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Q9
Q10
Q11
Q12
Secu
rity
Sco
re
Human Game Theory
59
Field Test Against Adversaries Mock Attackers
Example from PROTECT
ldquoMock attackerrdquo team deployed in Boston
Comparing PRE- to POST-PROTECT ldquodeterrencerdquo improved
Additional real-world indicators from Boston
Boston boaters questions
ldquohas the Coast Guard recently acquired more boatsrdquo
POST-PROTECT Actual reports of illegal activity
55
5956
Field Tests Against Adversaries
Computational Game Theory in the Field
Game theory vs Random
21 days of patrol
Identical conditions
Random + Human
Not controlled
0
20
40
60
80
100 Miscellaneous
Drugs
Firearm Violations
0
5
10
15
20
Captures30 min
Warnings30 min
Violations30 min
Game Theory
Rand+Human
Controlled
59
Expert Evaluation
Example from ARMOR IRIS amp PROTECT
February 2009 Commendations
LAX Police (City of Los Angeles)
July 2011 Operational Excellence
Award (US Coast Guard Boston)
September 2011 Certificate of
Appreciation (Federal Air Marshals)
June 2013 Meritorious Team Commendation
from Commandant (US Coast Guard)
57
59
Summary Security Games
Decision aids based on computational game theory in daily use
Optimize limited security resources against adversaries
Applications yield research challenges Science of security games
Scale-up Incremental strategy generation amp Marginals
Uncertainty Integrate MDPs Robustness Quantal response
Current applications (wildlife security) Interdisciplinar challenge
Global challenges Merge planninglearning amp security games
58
59
Just the Beginning of ldquoSecurity Gamesrdquohellip
Our next steps
59
Thank you
to sponsors
StartupARMORWAY Game theory in the field
Follow on research amp applications
Privacy audits
(Sinha IJCAIrsquo13)
Software testing
(Kukreja ASErsquo13)
Sport event security
(Yin AAAIrsquo14)
Singapore train
(Varakantham IAAIrsquo13)
Exam questions
(Li IJCAIrsquo13)
59
THANK YOU
tambeuscedu
httpteamcoreuscedusecurity
60
59
Port Security Threat Scenarios
US Ports $315 trillion economy
USS Cole after suicide attack
French oil tanker hit by small boat
Attack on a ferry
26
59
PROTECT Randomized Patrol Scheduling [2013]
27
Port Protection (Scale-up) and Ferries (Continuous Spacetime)
59
PROTECT Randomized Patrol Scheduling [2013]Port Protection (Scale-up) and Ferries (Continuous Spacetime)
28
59
Ferries Scale-up with Mobile Resources amp Moving TargetsTransition Graph Representation
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
29
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Ferries Scale-up with Mobile Resources amp Moving TargetsTransition Graph Representation
30
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
31
Patrols protect nearby ferry location Solve as done in ARMOR
Ferries Patrol RoutesExponential Numbers of Patrol Routes
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Patrols protect nearby ferry location Solve as done in ARMOR
Ferries Patrol RoutesExponential Numbers of Patrol Routes
32
Pr([(B5) (C 10) (C15)]) = 017
Pr([(B5) (C10) (B15)]) =013
NT variables
Pr([(A5) (A10) (B15)]) = 007
Pr([(A5) (A10) (A15)]) = 003
Jiang KiekintveldFang
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Variables NOT routes but probability flow over each segment
Ferries Scale-up Marginal Probabilities Over Segments
33
NT variables
Jiang KiekintveldFang
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
030
017
013
Obtain marginal probabilities over segments
010
007
003
N2T variablesExtract Pr([(B5) (C 10) (C15)]) = 017
Pr([(B5) (C10) (B15)]) =013
Ferries Scale-up with Marginals Over Separable SegmentsSignificant Speedup
34
NT variables
59
5936
Outline ldquoSecurity Gamesrdquo Research (2007-Now)
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
Evaluation II Real-world deployments
(Patience)
Evaluation I Scale up Handle uncertainty
59
TRUSTS Frequent adversary interaction games
Patrols Against Fare Evaders
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
017
013
010
010
37
59
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
38
Jiang Delle Fave
59
Markov Decision Problems in Security games
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
39
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution Jiang Delle Fave
5940
Urban Transportation Security
MDPs amp DEC-MDPs in Security Games
COPS LA Metro System(Against Opportunistic Crime)
STREETS Singapore Roads(Against Reckless Driving)
DEC-MDPs in Security Games Teamwork + Uncertainty
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
ZhangShieh
59
Uncertainty Space Algorithms
Bayesian and Robust Approaches
Adversary payoff uncertainty
Adversary observation amp
defender execution
uncertainty
Adversary rationality
uncertainty
RECON
BRASS
Monotonic Maximin
(Monotonic adversary)
41
URAC
Payoff interval
Not point
estimateGMC HUNTER
Bayesian
Robust
Yin Nguyen An
0
05
1
15
5 10
Def
end
ers
EU
Targets
ISG RECON URAC
59
Security Games Environmental Crime
amp Bounded Rationality
FisheryGulf of Mexico
Nakai Nam Theun
Forest Area Laos
WildlifeQueen Elizabeth National
ParkUganda
No patrols
Higher
density
Lower densityx
Yang Ford Brown
59
Uncertainty in Adversary Decision Bounded Rationality
Human Subjects as Poachers
59
Uncertainty in Adversary Decision[2009]
Human subjects Anchoring e-Optimality
-4
-3
-2
-1
0
1
Unobserved 5 Observations 20 Observations Unlimited
Average expected reward
DOBSS
Uniform
COBRA
ARMOR
ARMOR Outperforms uniform random
COBRA
MqxCaq
Xxxts
qxR
ji
Xi
ijj
Xi Qj
jiijqx
)1()()1(
|)|1()1(
max
ee
Anchoring
e-optimality
With Sarit Kraus 44
Pita
5945
Quantal Response Stochastic choice better choice more likely
Quantal Response Model of Adversary [2011] Not Maximize Expected Utility
T
j
jxadversary
EU
jxadversary
EU
j
e
eq
1
))((
))((
-3
-25
-2
-15
-1
-05
0
05
1
15
2
Payoff 1 Payoff 2 Payoff 3 Payoff 4
QuantalResponse
COBRA
ARMOR
Adversaryrsquos probability
of choosing target j
Yang
10
)(max1
1
)(
)(
t tt
defendT
jT
j
jxEU
jxEU
x
xKxts
jxEU
e
e
adversary
adversary
59
Uncertainty in Adversary Decision [2012]
Robust vs Modeling Adversaries
Robustness Bound loss to defender Not model attacker via QR
Defeating Robust Learned subjective utility
Robust wins Draw QR wins
42 52 6
Results on 100 games
SU-QR wins Draw Robust
13 8 1
Results on 22 games
MqxCa
qxR
ji
Xi
ij
jiij
Xi Qj
qx
)1()(0
max
β (Adversaryrsquos utility loss if
deviates from optimal) gt=
(Defenderrsquos utility loss due to
adversary deviation)
penaltyattackw
rewardattackw
probcapturewjaSEU
3
2
1)(
M
j
jxSEU
jxSEU
jadversary
adversary
e
eq
1
)(
)(
SU-QR wins Draw Robust
6 13 3
Results against security experts
With Sarit Kraus 46
5947
PAWS Protection Assistant for Wildlife Security[2014]Repeated Stackelberg Game
Simulation
Learn from crime data
Improve model
Defender calculates
mixed strategy
Defender executes
randomized patrols
Poachers attack targets
119890119878119864119880119894(w1w2w3)
119896 119890119878119864119880119896(w1w2w3)
Bayesian SUQR Heterogeneous Poachers
5948
PAWS Test April 2014
Trials in Queen Elizabeth National Park
Andrew Lemieux with rangers
on PAWS patrol in Uganda
59
Security Resource OptimizationEvaluating Deployed Security Systems Not Easy
Game theory Improvement over previous approaches
Previous Human schedulers or ldquosimple randomrdquo
49
Lab
Evaluation
Simulated
adversary
Human subject
adversaries
Field Evaluation
Patrol quality
Unpredictable Cover
Compare real schedules
Scheduling competition
Expert evaluation
Field Evaluation
Tests against adversaries
ldquoMock attackersrdquo
Capture rates of
real adversaries
59
Why Does Game Theory Perform Better
Weaknesses of Previous Methods
Human schedulers
Predictable patterns eg US Coast Guard
Scheduling effort amp cognitive burden
Simple random (eg dice roll)
Wrong weightscoverage eg officers to sparsely crowded terminals
No adversary reactions
Multiple deployments over multiple years without us forcing them
50
59
Lab Evaluation via Simulations
Example from IRIS (FAMS)
-10
-8
-6
-4
-2
0
2
4
6
50 150 250
Defe
nde
r E
xpe
cte
d u
tilit
y
Schedule Size
Uniform Weighted random 1 Weighted random 2 IRIS
51
59
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage
Patrols Before PROTECT Boston Patrols After PROTECT Boston
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Base Patrol Area
52
PROTECT (Coast Guard) 350 increase defender expected utility
59
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage for Less Effort
53
IRIS for FAMS Outperformed expert human over six monthsReportGAO-09-903T
ARMOR at LAX Savings of up to an hour a day in scheduling
59
Field Evaluation Human vs Game Theory Competition
Counter-terrorism Patrol Scheduling
90 officers on LA Metro Trains
Humans required significant effort
Worse schedules than game theory
Observerrsquos report on questions
54
3
35
4
45
5
55
Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Q9
Q10
Q11
Q12
Secu
rity
Sco
re
Human Game Theory
59
Field Test Against Adversaries Mock Attackers
Example from PROTECT
ldquoMock attackerrdquo team deployed in Boston
Comparing PRE- to POST-PROTECT ldquodeterrencerdquo improved
Additional real-world indicators from Boston
Boston boaters questions
ldquohas the Coast Guard recently acquired more boatsrdquo
POST-PROTECT Actual reports of illegal activity
55
5956
Field Tests Against Adversaries
Computational Game Theory in the Field
Game theory vs Random
21 days of patrol
Identical conditions
Random + Human
Not controlled
0
20
40
60
80
100 Miscellaneous
Drugs
Firearm Violations
0
5
10
15
20
Captures30 min
Warnings30 min
Violations30 min
Game Theory
Rand+Human
Controlled
59
Expert Evaluation
Example from ARMOR IRIS amp PROTECT
February 2009 Commendations
LAX Police (City of Los Angeles)
July 2011 Operational Excellence
Award (US Coast Guard Boston)
September 2011 Certificate of
Appreciation (Federal Air Marshals)
June 2013 Meritorious Team Commendation
from Commandant (US Coast Guard)
57
59
Summary Security Games
Decision aids based on computational game theory in daily use
Optimize limited security resources against adversaries
Applications yield research challenges Science of security games
Scale-up Incremental strategy generation amp Marginals
Uncertainty Integrate MDPs Robustness Quantal response
Current applications (wildlife security) Interdisciplinar challenge
Global challenges Merge planninglearning amp security games
58
59
Just the Beginning of ldquoSecurity Gamesrdquohellip
Our next steps
59
Thank you
to sponsors
StartupARMORWAY Game theory in the field
Follow on research amp applications
Privacy audits
(Sinha IJCAIrsquo13)
Software testing
(Kukreja ASErsquo13)
Sport event security
(Yin AAAIrsquo14)
Singapore train
(Varakantham IAAIrsquo13)
Exam questions
(Li IJCAIrsquo13)
59
THANK YOU
tambeuscedu
httpteamcoreuscedusecurity
60
59
PROTECT Randomized Patrol Scheduling [2013]
27
Port Protection (Scale-up) and Ferries (Continuous Spacetime)
59
PROTECT Randomized Patrol Scheduling [2013]Port Protection (Scale-up) and Ferries (Continuous Spacetime)
28
59
Ferries Scale-up with Mobile Resources amp Moving TargetsTransition Graph Representation
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
29
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Ferries Scale-up with Mobile Resources amp Moving TargetsTransition Graph Representation
30
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
31
Patrols protect nearby ferry location Solve as done in ARMOR
Ferries Patrol RoutesExponential Numbers of Patrol Routes
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Patrols protect nearby ferry location Solve as done in ARMOR
Ferries Patrol RoutesExponential Numbers of Patrol Routes
32
Pr([(B5) (C 10) (C15)]) = 017
Pr([(B5) (C10) (B15)]) =013
NT variables
Pr([(A5) (A10) (B15)]) = 007
Pr([(A5) (A10) (A15)]) = 003
Jiang KiekintveldFang
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Variables NOT routes but probability flow over each segment
Ferries Scale-up Marginal Probabilities Over Segments
33
NT variables
Jiang KiekintveldFang
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
030
017
013
Obtain marginal probabilities over segments
010
007
003
N2T variablesExtract Pr([(B5) (C 10) (C15)]) = 017
Pr([(B5) (C10) (B15)]) =013
Ferries Scale-up with Marginals Over Separable SegmentsSignificant Speedup
34
NT variables
59
5936
Outline ldquoSecurity Gamesrdquo Research (2007-Now)
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
Evaluation II Real-world deployments
(Patience)
Evaluation I Scale up Handle uncertainty
59
TRUSTS Frequent adversary interaction games
Patrols Against Fare Evaders
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
017
013
010
010
37
59
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
38
Jiang Delle Fave
59
Markov Decision Problems in Security games
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
39
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution Jiang Delle Fave
5940
Urban Transportation Security
MDPs amp DEC-MDPs in Security Games
COPS LA Metro System(Against Opportunistic Crime)
STREETS Singapore Roads(Against Reckless Driving)
DEC-MDPs in Security Games Teamwork + Uncertainty
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
ZhangShieh
59
Uncertainty Space Algorithms
Bayesian and Robust Approaches
Adversary payoff uncertainty
Adversary observation amp
defender execution
uncertainty
Adversary rationality
uncertainty
RECON
BRASS
Monotonic Maximin
(Monotonic adversary)
41
URAC
Payoff interval
Not point
estimateGMC HUNTER
Bayesian
Robust
Yin Nguyen An
0
05
1
15
5 10
Def
end
ers
EU
Targets
ISG RECON URAC
59
Security Games Environmental Crime
amp Bounded Rationality
FisheryGulf of Mexico
Nakai Nam Theun
Forest Area Laos
WildlifeQueen Elizabeth National
ParkUganda
No patrols
Higher
density
Lower densityx
Yang Ford Brown
59
Uncertainty in Adversary Decision Bounded Rationality
Human Subjects as Poachers
59
Uncertainty in Adversary Decision[2009]
Human subjects Anchoring e-Optimality
-4
-3
-2
-1
0
1
Unobserved 5 Observations 20 Observations Unlimited
Average expected reward
DOBSS
Uniform
COBRA
ARMOR
ARMOR Outperforms uniform random
COBRA
MqxCaq
Xxxts
qxR
ji
Xi
ijj
Xi Qj
jiijqx
)1()()1(
|)|1()1(
max
ee
Anchoring
e-optimality
With Sarit Kraus 44
Pita
5945
Quantal Response Stochastic choice better choice more likely
Quantal Response Model of Adversary [2011] Not Maximize Expected Utility
T
j
jxadversary
EU
jxadversary
EU
j
e
eq
1
))((
))((
-3
-25
-2
-15
-1
-05
0
05
1
15
2
Payoff 1 Payoff 2 Payoff 3 Payoff 4
QuantalResponse
COBRA
ARMOR
Adversaryrsquos probability
of choosing target j
Yang
10
)(max1
1
)(
)(
t tt
defendT
jT
j
jxEU
jxEU
x
xKxts
jxEU
e
e
adversary
adversary
59
Uncertainty in Adversary Decision [2012]
Robust vs Modeling Adversaries
Robustness Bound loss to defender Not model attacker via QR
Defeating Robust Learned subjective utility
Robust wins Draw QR wins
42 52 6
Results on 100 games
SU-QR wins Draw Robust
13 8 1
Results on 22 games
MqxCa
qxR
ji
Xi
ij
jiij
Xi Qj
qx
)1()(0
max
β (Adversaryrsquos utility loss if
deviates from optimal) gt=
(Defenderrsquos utility loss due to
adversary deviation)
penaltyattackw
rewardattackw
probcapturewjaSEU
3
2
1)(
M
j
jxSEU
jxSEU
jadversary
adversary
e
eq
1
)(
)(
SU-QR wins Draw Robust
6 13 3
Results against security experts
With Sarit Kraus 46
5947
PAWS Protection Assistant for Wildlife Security[2014]Repeated Stackelberg Game
Simulation
Learn from crime data
Improve model
Defender calculates
mixed strategy
Defender executes
randomized patrols
Poachers attack targets
119890119878119864119880119894(w1w2w3)
119896 119890119878119864119880119896(w1w2w3)
Bayesian SUQR Heterogeneous Poachers
5948
PAWS Test April 2014
Trials in Queen Elizabeth National Park
Andrew Lemieux with rangers
on PAWS patrol in Uganda
59
Security Resource OptimizationEvaluating Deployed Security Systems Not Easy
Game theory Improvement over previous approaches
Previous Human schedulers or ldquosimple randomrdquo
49
Lab
Evaluation
Simulated
adversary
Human subject
adversaries
Field Evaluation
Patrol quality
Unpredictable Cover
Compare real schedules
Scheduling competition
Expert evaluation
Field Evaluation
Tests against adversaries
ldquoMock attackersrdquo
Capture rates of
real adversaries
59
Why Does Game Theory Perform Better
Weaknesses of Previous Methods
Human schedulers
Predictable patterns eg US Coast Guard
Scheduling effort amp cognitive burden
Simple random (eg dice roll)
Wrong weightscoverage eg officers to sparsely crowded terminals
No adversary reactions
Multiple deployments over multiple years without us forcing them
50
59
Lab Evaluation via Simulations
Example from IRIS (FAMS)
-10
-8
-6
-4
-2
0
2
4
6
50 150 250
Defe
nde
r E
xpe
cte
d u
tilit
y
Schedule Size
Uniform Weighted random 1 Weighted random 2 IRIS
51
59
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage
Patrols Before PROTECT Boston Patrols After PROTECT Boston
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Base Patrol Area
52
PROTECT (Coast Guard) 350 increase defender expected utility
59
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage for Less Effort
53
IRIS for FAMS Outperformed expert human over six monthsReportGAO-09-903T
ARMOR at LAX Savings of up to an hour a day in scheduling
59
Field Evaluation Human vs Game Theory Competition
Counter-terrorism Patrol Scheduling
90 officers on LA Metro Trains
Humans required significant effort
Worse schedules than game theory
Observerrsquos report on questions
54
3
35
4
45
5
55
Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Q9
Q10
Q11
Q12
Secu
rity
Sco
re
Human Game Theory
59
Field Test Against Adversaries Mock Attackers
Example from PROTECT
ldquoMock attackerrdquo team deployed in Boston
Comparing PRE- to POST-PROTECT ldquodeterrencerdquo improved
Additional real-world indicators from Boston
Boston boaters questions
ldquohas the Coast Guard recently acquired more boatsrdquo
POST-PROTECT Actual reports of illegal activity
55
5956
Field Tests Against Adversaries
Computational Game Theory in the Field
Game theory vs Random
21 days of patrol
Identical conditions
Random + Human
Not controlled
0
20
40
60
80
100 Miscellaneous
Drugs
Firearm Violations
0
5
10
15
20
Captures30 min
Warnings30 min
Violations30 min
Game Theory
Rand+Human
Controlled
59
Expert Evaluation
Example from ARMOR IRIS amp PROTECT
February 2009 Commendations
LAX Police (City of Los Angeles)
July 2011 Operational Excellence
Award (US Coast Guard Boston)
September 2011 Certificate of
Appreciation (Federal Air Marshals)
June 2013 Meritorious Team Commendation
from Commandant (US Coast Guard)
57
59
Summary Security Games
Decision aids based on computational game theory in daily use
Optimize limited security resources against adversaries
Applications yield research challenges Science of security games
Scale-up Incremental strategy generation amp Marginals
Uncertainty Integrate MDPs Robustness Quantal response
Current applications (wildlife security) Interdisciplinar challenge
Global challenges Merge planninglearning amp security games
58
59
Just the Beginning of ldquoSecurity Gamesrdquohellip
Our next steps
59
Thank you
to sponsors
StartupARMORWAY Game theory in the field
Follow on research amp applications
Privacy audits
(Sinha IJCAIrsquo13)
Software testing
(Kukreja ASErsquo13)
Sport event security
(Yin AAAIrsquo14)
Singapore train
(Varakantham IAAIrsquo13)
Exam questions
(Li IJCAIrsquo13)
59
THANK YOU
tambeuscedu
httpteamcoreuscedusecurity
60
59
PROTECT Randomized Patrol Scheduling [2013]Port Protection (Scale-up) and Ferries (Continuous Spacetime)
28
59
Ferries Scale-up with Mobile Resources amp Moving TargetsTransition Graph Representation
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
29
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Ferries Scale-up with Mobile Resources amp Moving TargetsTransition Graph Representation
30
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
31
Patrols protect nearby ferry location Solve as done in ARMOR
Ferries Patrol RoutesExponential Numbers of Patrol Routes
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Patrols protect nearby ferry location Solve as done in ARMOR
Ferries Patrol RoutesExponential Numbers of Patrol Routes
32
Pr([(B5) (C 10) (C15)]) = 017
Pr([(B5) (C10) (B15)]) =013
NT variables
Pr([(A5) (A10) (B15)]) = 007
Pr([(A5) (A10) (A15)]) = 003
Jiang KiekintveldFang
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Variables NOT routes but probability flow over each segment
Ferries Scale-up Marginal Probabilities Over Segments
33
NT variables
Jiang KiekintveldFang
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
030
017
013
Obtain marginal probabilities over segments
010
007
003
N2T variablesExtract Pr([(B5) (C 10) (C15)]) = 017
Pr([(B5) (C10) (B15)]) =013
Ferries Scale-up with Marginals Over Separable SegmentsSignificant Speedup
34
NT variables
59
5936
Outline ldquoSecurity Gamesrdquo Research (2007-Now)
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
Evaluation II Real-world deployments
(Patience)
Evaluation I Scale up Handle uncertainty
59
TRUSTS Frequent adversary interaction games
Patrols Against Fare Evaders
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
017
013
010
010
37
59
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
38
Jiang Delle Fave
59
Markov Decision Problems in Security games
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
39
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution Jiang Delle Fave
5940
Urban Transportation Security
MDPs amp DEC-MDPs in Security Games
COPS LA Metro System(Against Opportunistic Crime)
STREETS Singapore Roads(Against Reckless Driving)
DEC-MDPs in Security Games Teamwork + Uncertainty
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
ZhangShieh
59
Uncertainty Space Algorithms
Bayesian and Robust Approaches
Adversary payoff uncertainty
Adversary observation amp
defender execution
uncertainty
Adversary rationality
uncertainty
RECON
BRASS
Monotonic Maximin
(Monotonic adversary)
41
URAC
Payoff interval
Not point
estimateGMC HUNTER
Bayesian
Robust
Yin Nguyen An
0
05
1
15
5 10
Def
end
ers
EU
Targets
ISG RECON URAC
59
Security Games Environmental Crime
amp Bounded Rationality
FisheryGulf of Mexico
Nakai Nam Theun
Forest Area Laos
WildlifeQueen Elizabeth National
ParkUganda
No patrols
Higher
density
Lower densityx
Yang Ford Brown
59
Uncertainty in Adversary Decision Bounded Rationality
Human Subjects as Poachers
59
Uncertainty in Adversary Decision[2009]
Human subjects Anchoring e-Optimality
-4
-3
-2
-1
0
1
Unobserved 5 Observations 20 Observations Unlimited
Average expected reward
DOBSS
Uniform
COBRA
ARMOR
ARMOR Outperforms uniform random
COBRA
MqxCaq
Xxxts
qxR
ji
Xi
ijj
Xi Qj
jiijqx
)1()()1(
|)|1()1(
max
ee
Anchoring
e-optimality
With Sarit Kraus 44
Pita
5945
Quantal Response Stochastic choice better choice more likely
Quantal Response Model of Adversary [2011] Not Maximize Expected Utility
T
j
jxadversary
EU
jxadversary
EU
j
e
eq
1
))((
))((
-3
-25
-2
-15
-1
-05
0
05
1
15
2
Payoff 1 Payoff 2 Payoff 3 Payoff 4
QuantalResponse
COBRA
ARMOR
Adversaryrsquos probability
of choosing target j
Yang
10
)(max1
1
)(
)(
t tt
defendT
jT
j
jxEU
jxEU
x
xKxts
jxEU
e
e
adversary
adversary
59
Uncertainty in Adversary Decision [2012]
Robust vs Modeling Adversaries
Robustness Bound loss to defender Not model attacker via QR
Defeating Robust Learned subjective utility
Robust wins Draw QR wins
42 52 6
Results on 100 games
SU-QR wins Draw Robust
13 8 1
Results on 22 games
MqxCa
qxR
ji
Xi
ij
jiij
Xi Qj
qx
)1()(0
max
β (Adversaryrsquos utility loss if
deviates from optimal) gt=
(Defenderrsquos utility loss due to
adversary deviation)
penaltyattackw
rewardattackw
probcapturewjaSEU
3
2
1)(
M
j
jxSEU
jxSEU
jadversary
adversary
e
eq
1
)(
)(
SU-QR wins Draw Robust
6 13 3
Results against security experts
With Sarit Kraus 46
5947
PAWS Protection Assistant for Wildlife Security[2014]Repeated Stackelberg Game
Simulation
Learn from crime data
Improve model
Defender calculates
mixed strategy
Defender executes
randomized patrols
Poachers attack targets
119890119878119864119880119894(w1w2w3)
119896 119890119878119864119880119896(w1w2w3)
Bayesian SUQR Heterogeneous Poachers
5948
PAWS Test April 2014
Trials in Queen Elizabeth National Park
Andrew Lemieux with rangers
on PAWS patrol in Uganda
59
Security Resource OptimizationEvaluating Deployed Security Systems Not Easy
Game theory Improvement over previous approaches
Previous Human schedulers or ldquosimple randomrdquo
49
Lab
Evaluation
Simulated
adversary
Human subject
adversaries
Field Evaluation
Patrol quality
Unpredictable Cover
Compare real schedules
Scheduling competition
Expert evaluation
Field Evaluation
Tests against adversaries
ldquoMock attackersrdquo
Capture rates of
real adversaries
59
Why Does Game Theory Perform Better
Weaknesses of Previous Methods
Human schedulers
Predictable patterns eg US Coast Guard
Scheduling effort amp cognitive burden
Simple random (eg dice roll)
Wrong weightscoverage eg officers to sparsely crowded terminals
No adversary reactions
Multiple deployments over multiple years without us forcing them
50
59
Lab Evaluation via Simulations
Example from IRIS (FAMS)
-10
-8
-6
-4
-2
0
2
4
6
50 150 250
Defe
nde
r E
xpe
cte
d u
tilit
y
Schedule Size
Uniform Weighted random 1 Weighted random 2 IRIS
51
59
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage
Patrols Before PROTECT Boston Patrols After PROTECT Boston
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Base Patrol Area
52
PROTECT (Coast Guard) 350 increase defender expected utility
59
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage for Less Effort
53
IRIS for FAMS Outperformed expert human over six monthsReportGAO-09-903T
ARMOR at LAX Savings of up to an hour a day in scheduling
59
Field Evaluation Human vs Game Theory Competition
Counter-terrorism Patrol Scheduling
90 officers on LA Metro Trains
Humans required significant effort
Worse schedules than game theory
Observerrsquos report on questions
54
3
35
4
45
5
55
Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Q9
Q10
Q11
Q12
Secu
rity
Sco
re
Human Game Theory
59
Field Test Against Adversaries Mock Attackers
Example from PROTECT
ldquoMock attackerrdquo team deployed in Boston
Comparing PRE- to POST-PROTECT ldquodeterrencerdquo improved
Additional real-world indicators from Boston
Boston boaters questions
ldquohas the Coast Guard recently acquired more boatsrdquo
POST-PROTECT Actual reports of illegal activity
55
5956
Field Tests Against Adversaries
Computational Game Theory in the Field
Game theory vs Random
21 days of patrol
Identical conditions
Random + Human
Not controlled
0
20
40
60
80
100 Miscellaneous
Drugs
Firearm Violations
0
5
10
15
20
Captures30 min
Warnings30 min
Violations30 min
Game Theory
Rand+Human
Controlled
59
Expert Evaluation
Example from ARMOR IRIS amp PROTECT
February 2009 Commendations
LAX Police (City of Los Angeles)
July 2011 Operational Excellence
Award (US Coast Guard Boston)
September 2011 Certificate of
Appreciation (Federal Air Marshals)
June 2013 Meritorious Team Commendation
from Commandant (US Coast Guard)
57
59
Summary Security Games
Decision aids based on computational game theory in daily use
Optimize limited security resources against adversaries
Applications yield research challenges Science of security games
Scale-up Incremental strategy generation amp Marginals
Uncertainty Integrate MDPs Robustness Quantal response
Current applications (wildlife security) Interdisciplinar challenge
Global challenges Merge planninglearning amp security games
58
59
Just the Beginning of ldquoSecurity Gamesrdquohellip
Our next steps
59
Thank you
to sponsors
StartupARMORWAY Game theory in the field
Follow on research amp applications
Privacy audits
(Sinha IJCAIrsquo13)
Software testing
(Kukreja ASErsquo13)
Sport event security
(Yin AAAIrsquo14)
Singapore train
(Varakantham IAAIrsquo13)
Exam questions
(Li IJCAIrsquo13)
59
THANK YOU
tambeuscedu
httpteamcoreuscedusecurity
60
59
Ferries Scale-up with Mobile Resources amp Moving TargetsTransition Graph Representation
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
29
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Ferries Scale-up with Mobile Resources amp Moving TargetsTransition Graph Representation
30
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
31
Patrols protect nearby ferry location Solve as done in ARMOR
Ferries Patrol RoutesExponential Numbers of Patrol Routes
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Patrols protect nearby ferry location Solve as done in ARMOR
Ferries Patrol RoutesExponential Numbers of Patrol Routes
32
Pr([(B5) (C 10) (C15)]) = 017
Pr([(B5) (C10) (B15)]) =013
NT variables
Pr([(A5) (A10) (B15)]) = 007
Pr([(A5) (A10) (A15)]) = 003
Jiang KiekintveldFang
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Variables NOT routes but probability flow over each segment
Ferries Scale-up Marginal Probabilities Over Segments
33
NT variables
Jiang KiekintveldFang
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
030
017
013
Obtain marginal probabilities over segments
010
007
003
N2T variablesExtract Pr([(B5) (C 10) (C15)]) = 017
Pr([(B5) (C10) (B15)]) =013
Ferries Scale-up with Marginals Over Separable SegmentsSignificant Speedup
34
NT variables
59
5936
Outline ldquoSecurity Gamesrdquo Research (2007-Now)
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
Evaluation II Real-world deployments
(Patience)
Evaluation I Scale up Handle uncertainty
59
TRUSTS Frequent adversary interaction games
Patrols Against Fare Evaders
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
017
013
010
010
37
59
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
38
Jiang Delle Fave
59
Markov Decision Problems in Security games
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
39
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution Jiang Delle Fave
5940
Urban Transportation Security
MDPs amp DEC-MDPs in Security Games
COPS LA Metro System(Against Opportunistic Crime)
STREETS Singapore Roads(Against Reckless Driving)
DEC-MDPs in Security Games Teamwork + Uncertainty
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
ZhangShieh
59
Uncertainty Space Algorithms
Bayesian and Robust Approaches
Adversary payoff uncertainty
Adversary observation amp
defender execution
uncertainty
Adversary rationality
uncertainty
RECON
BRASS
Monotonic Maximin
(Monotonic adversary)
41
URAC
Payoff interval
Not point
estimateGMC HUNTER
Bayesian
Robust
Yin Nguyen An
0
05
1
15
5 10
Def
end
ers
EU
Targets
ISG RECON URAC
59
Security Games Environmental Crime
amp Bounded Rationality
FisheryGulf of Mexico
Nakai Nam Theun
Forest Area Laos
WildlifeQueen Elizabeth National
ParkUganda
No patrols
Higher
density
Lower densityx
Yang Ford Brown
59
Uncertainty in Adversary Decision Bounded Rationality
Human Subjects as Poachers
59
Uncertainty in Adversary Decision[2009]
Human subjects Anchoring e-Optimality
-4
-3
-2
-1
0
1
Unobserved 5 Observations 20 Observations Unlimited
Average expected reward
DOBSS
Uniform
COBRA
ARMOR
ARMOR Outperforms uniform random
COBRA
MqxCaq
Xxxts
qxR
ji
Xi
ijj
Xi Qj
jiijqx
)1()()1(
|)|1()1(
max
ee
Anchoring
e-optimality
With Sarit Kraus 44
Pita
5945
Quantal Response Stochastic choice better choice more likely
Quantal Response Model of Adversary [2011] Not Maximize Expected Utility
T
j
jxadversary
EU
jxadversary
EU
j
e
eq
1
))((
))((
-3
-25
-2
-15
-1
-05
0
05
1
15
2
Payoff 1 Payoff 2 Payoff 3 Payoff 4
QuantalResponse
COBRA
ARMOR
Adversaryrsquos probability
of choosing target j
Yang
10
)(max1
1
)(
)(
t tt
defendT
jT
j
jxEU
jxEU
x
xKxts
jxEU
e
e
adversary
adversary
59
Uncertainty in Adversary Decision [2012]
Robust vs Modeling Adversaries
Robustness Bound loss to defender Not model attacker via QR
Defeating Robust Learned subjective utility
Robust wins Draw QR wins
42 52 6
Results on 100 games
SU-QR wins Draw Robust
13 8 1
Results on 22 games
MqxCa
qxR
ji
Xi
ij
jiij
Xi Qj
qx
)1()(0
max
β (Adversaryrsquos utility loss if
deviates from optimal) gt=
(Defenderrsquos utility loss due to
adversary deviation)
penaltyattackw
rewardattackw
probcapturewjaSEU
3
2
1)(
M
j
jxSEU
jxSEU
jadversary
adversary
e
eq
1
)(
)(
SU-QR wins Draw Robust
6 13 3
Results against security experts
With Sarit Kraus 46
5947
PAWS Protection Assistant for Wildlife Security[2014]Repeated Stackelberg Game
Simulation
Learn from crime data
Improve model
Defender calculates
mixed strategy
Defender executes
randomized patrols
Poachers attack targets
119890119878119864119880119894(w1w2w3)
119896 119890119878119864119880119896(w1w2w3)
Bayesian SUQR Heterogeneous Poachers
5948
PAWS Test April 2014
Trials in Queen Elizabeth National Park
Andrew Lemieux with rangers
on PAWS patrol in Uganda
59
Security Resource OptimizationEvaluating Deployed Security Systems Not Easy
Game theory Improvement over previous approaches
Previous Human schedulers or ldquosimple randomrdquo
49
Lab
Evaluation
Simulated
adversary
Human subject
adversaries
Field Evaluation
Patrol quality
Unpredictable Cover
Compare real schedules
Scheduling competition
Expert evaluation
Field Evaluation
Tests against adversaries
ldquoMock attackersrdquo
Capture rates of
real adversaries
59
Why Does Game Theory Perform Better
Weaknesses of Previous Methods
Human schedulers
Predictable patterns eg US Coast Guard
Scheduling effort amp cognitive burden
Simple random (eg dice roll)
Wrong weightscoverage eg officers to sparsely crowded terminals
No adversary reactions
Multiple deployments over multiple years without us forcing them
50
59
Lab Evaluation via Simulations
Example from IRIS (FAMS)
-10
-8
-6
-4
-2
0
2
4
6
50 150 250
Defe
nde
r E
xpe
cte
d u
tilit
y
Schedule Size
Uniform Weighted random 1 Weighted random 2 IRIS
51
59
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage
Patrols Before PROTECT Boston Patrols After PROTECT Boston
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Base Patrol Area
52
PROTECT (Coast Guard) 350 increase defender expected utility
59
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage for Less Effort
53
IRIS for FAMS Outperformed expert human over six monthsReportGAO-09-903T
ARMOR at LAX Savings of up to an hour a day in scheduling
59
Field Evaluation Human vs Game Theory Competition
Counter-terrorism Patrol Scheduling
90 officers on LA Metro Trains
Humans required significant effort
Worse schedules than game theory
Observerrsquos report on questions
54
3
35
4
45
5
55
Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Q9
Q10
Q11
Q12
Secu
rity
Sco
re
Human Game Theory
59
Field Test Against Adversaries Mock Attackers
Example from PROTECT
ldquoMock attackerrdquo team deployed in Boston
Comparing PRE- to POST-PROTECT ldquodeterrencerdquo improved
Additional real-world indicators from Boston
Boston boaters questions
ldquohas the Coast Guard recently acquired more boatsrdquo
POST-PROTECT Actual reports of illegal activity
55
5956
Field Tests Against Adversaries
Computational Game Theory in the Field
Game theory vs Random
21 days of patrol
Identical conditions
Random + Human
Not controlled
0
20
40
60
80
100 Miscellaneous
Drugs
Firearm Violations
0
5
10
15
20
Captures30 min
Warnings30 min
Violations30 min
Game Theory
Rand+Human
Controlled
59
Expert Evaluation
Example from ARMOR IRIS amp PROTECT
February 2009 Commendations
LAX Police (City of Los Angeles)
July 2011 Operational Excellence
Award (US Coast Guard Boston)
September 2011 Certificate of
Appreciation (Federal Air Marshals)
June 2013 Meritorious Team Commendation
from Commandant (US Coast Guard)
57
59
Summary Security Games
Decision aids based on computational game theory in daily use
Optimize limited security resources against adversaries
Applications yield research challenges Science of security games
Scale-up Incremental strategy generation amp Marginals
Uncertainty Integrate MDPs Robustness Quantal response
Current applications (wildlife security) Interdisciplinar challenge
Global challenges Merge planninglearning amp security games
58
59
Just the Beginning of ldquoSecurity Gamesrdquohellip
Our next steps
59
Thank you
to sponsors
StartupARMORWAY Game theory in the field
Follow on research amp applications
Privacy audits
(Sinha IJCAIrsquo13)
Software testing
(Kukreja ASErsquo13)
Sport event security
(Yin AAAIrsquo14)
Singapore train
(Varakantham IAAIrsquo13)
Exam questions
(Li IJCAIrsquo13)
59
THANK YOU
tambeuscedu
httpteamcoreuscedusecurity
60
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Ferries Scale-up with Mobile Resources amp Moving TargetsTransition Graph Representation
30
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
31
Patrols protect nearby ferry location Solve as done in ARMOR
Ferries Patrol RoutesExponential Numbers of Patrol Routes
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Patrols protect nearby ferry location Solve as done in ARMOR
Ferries Patrol RoutesExponential Numbers of Patrol Routes
32
Pr([(B5) (C 10) (C15)]) = 017
Pr([(B5) (C10) (B15)]) =013
NT variables
Pr([(A5) (A10) (B15)]) = 007
Pr([(A5) (A10) (A15)]) = 003
Jiang KiekintveldFang
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Variables NOT routes but probability flow over each segment
Ferries Scale-up Marginal Probabilities Over Segments
33
NT variables
Jiang KiekintveldFang
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
030
017
013
Obtain marginal probabilities over segments
010
007
003
N2T variablesExtract Pr([(B5) (C 10) (C15)]) = 017
Pr([(B5) (C10) (B15)]) =013
Ferries Scale-up with Marginals Over Separable SegmentsSignificant Speedup
34
NT variables
59
5936
Outline ldquoSecurity Gamesrdquo Research (2007-Now)
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
Evaluation II Real-world deployments
(Patience)
Evaluation I Scale up Handle uncertainty
59
TRUSTS Frequent adversary interaction games
Patrols Against Fare Evaders
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
017
013
010
010
37
59
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
38
Jiang Delle Fave
59
Markov Decision Problems in Security games
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
39
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution Jiang Delle Fave
5940
Urban Transportation Security
MDPs amp DEC-MDPs in Security Games
COPS LA Metro System(Against Opportunistic Crime)
STREETS Singapore Roads(Against Reckless Driving)
DEC-MDPs in Security Games Teamwork + Uncertainty
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
ZhangShieh
59
Uncertainty Space Algorithms
Bayesian and Robust Approaches
Adversary payoff uncertainty
Adversary observation amp
defender execution
uncertainty
Adversary rationality
uncertainty
RECON
BRASS
Monotonic Maximin
(Monotonic adversary)
41
URAC
Payoff interval
Not point
estimateGMC HUNTER
Bayesian
Robust
Yin Nguyen An
0
05
1
15
5 10
Def
end
ers
EU
Targets
ISG RECON URAC
59
Security Games Environmental Crime
amp Bounded Rationality
FisheryGulf of Mexico
Nakai Nam Theun
Forest Area Laos
WildlifeQueen Elizabeth National
ParkUganda
No patrols
Higher
density
Lower densityx
Yang Ford Brown
59
Uncertainty in Adversary Decision Bounded Rationality
Human Subjects as Poachers
59
Uncertainty in Adversary Decision[2009]
Human subjects Anchoring e-Optimality
-4
-3
-2
-1
0
1
Unobserved 5 Observations 20 Observations Unlimited
Average expected reward
DOBSS
Uniform
COBRA
ARMOR
ARMOR Outperforms uniform random
COBRA
MqxCaq
Xxxts
qxR
ji
Xi
ijj
Xi Qj
jiijqx
)1()()1(
|)|1()1(
max
ee
Anchoring
e-optimality
With Sarit Kraus 44
Pita
5945
Quantal Response Stochastic choice better choice more likely
Quantal Response Model of Adversary [2011] Not Maximize Expected Utility
T
j
jxadversary
EU
jxadversary
EU
j
e
eq
1
))((
))((
-3
-25
-2
-15
-1
-05
0
05
1
15
2
Payoff 1 Payoff 2 Payoff 3 Payoff 4
QuantalResponse
COBRA
ARMOR
Adversaryrsquos probability
of choosing target j
Yang
10
)(max1
1
)(
)(
t tt
defendT
jT
j
jxEU
jxEU
x
xKxts
jxEU
e
e
adversary
adversary
59
Uncertainty in Adversary Decision [2012]
Robust vs Modeling Adversaries
Robustness Bound loss to defender Not model attacker via QR
Defeating Robust Learned subjective utility
Robust wins Draw QR wins
42 52 6
Results on 100 games
SU-QR wins Draw Robust
13 8 1
Results on 22 games
MqxCa
qxR
ji
Xi
ij
jiij
Xi Qj
qx
)1()(0
max
β (Adversaryrsquos utility loss if
deviates from optimal) gt=
(Defenderrsquos utility loss due to
adversary deviation)
penaltyattackw
rewardattackw
probcapturewjaSEU
3
2
1)(
M
j
jxSEU
jxSEU
jadversary
adversary
e
eq
1
)(
)(
SU-QR wins Draw Robust
6 13 3
Results against security experts
With Sarit Kraus 46
5947
PAWS Protection Assistant for Wildlife Security[2014]Repeated Stackelberg Game
Simulation
Learn from crime data
Improve model
Defender calculates
mixed strategy
Defender executes
randomized patrols
Poachers attack targets
119890119878119864119880119894(w1w2w3)
119896 119890119878119864119880119896(w1w2w3)
Bayesian SUQR Heterogeneous Poachers
5948
PAWS Test April 2014
Trials in Queen Elizabeth National Park
Andrew Lemieux with rangers
on PAWS patrol in Uganda
59
Security Resource OptimizationEvaluating Deployed Security Systems Not Easy
Game theory Improvement over previous approaches
Previous Human schedulers or ldquosimple randomrdquo
49
Lab
Evaluation
Simulated
adversary
Human subject
adversaries
Field Evaluation
Patrol quality
Unpredictable Cover
Compare real schedules
Scheduling competition
Expert evaluation
Field Evaluation
Tests against adversaries
ldquoMock attackersrdquo
Capture rates of
real adversaries
59
Why Does Game Theory Perform Better
Weaknesses of Previous Methods
Human schedulers
Predictable patterns eg US Coast Guard
Scheduling effort amp cognitive burden
Simple random (eg dice roll)
Wrong weightscoverage eg officers to sparsely crowded terminals
No adversary reactions
Multiple deployments over multiple years without us forcing them
50
59
Lab Evaluation via Simulations
Example from IRIS (FAMS)
-10
-8
-6
-4
-2
0
2
4
6
50 150 250
Defe
nde
r E
xpe
cte
d u
tilit
y
Schedule Size
Uniform Weighted random 1 Weighted random 2 IRIS
51
59
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage
Patrols Before PROTECT Boston Patrols After PROTECT Boston
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Base Patrol Area
52
PROTECT (Coast Guard) 350 increase defender expected utility
59
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage for Less Effort
53
IRIS for FAMS Outperformed expert human over six monthsReportGAO-09-903T
ARMOR at LAX Savings of up to an hour a day in scheduling
59
Field Evaluation Human vs Game Theory Competition
Counter-terrorism Patrol Scheduling
90 officers on LA Metro Trains
Humans required significant effort
Worse schedules than game theory
Observerrsquos report on questions
54
3
35
4
45
5
55
Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Q9
Q10
Q11
Q12
Secu
rity
Sco
re
Human Game Theory
59
Field Test Against Adversaries Mock Attackers
Example from PROTECT
ldquoMock attackerrdquo team deployed in Boston
Comparing PRE- to POST-PROTECT ldquodeterrencerdquo improved
Additional real-world indicators from Boston
Boston boaters questions
ldquohas the Coast Guard recently acquired more boatsrdquo
POST-PROTECT Actual reports of illegal activity
55
5956
Field Tests Against Adversaries
Computational Game Theory in the Field
Game theory vs Random
21 days of patrol
Identical conditions
Random + Human
Not controlled
0
20
40
60
80
100 Miscellaneous
Drugs
Firearm Violations
0
5
10
15
20
Captures30 min
Warnings30 min
Violations30 min
Game Theory
Rand+Human
Controlled
59
Expert Evaluation
Example from ARMOR IRIS amp PROTECT
February 2009 Commendations
LAX Police (City of Los Angeles)
July 2011 Operational Excellence
Award (US Coast Guard Boston)
September 2011 Certificate of
Appreciation (Federal Air Marshals)
June 2013 Meritorious Team Commendation
from Commandant (US Coast Guard)
57
59
Summary Security Games
Decision aids based on computational game theory in daily use
Optimize limited security resources against adversaries
Applications yield research challenges Science of security games
Scale-up Incremental strategy generation amp Marginals
Uncertainty Integrate MDPs Robustness Quantal response
Current applications (wildlife security) Interdisciplinar challenge
Global challenges Merge planninglearning amp security games
58
59
Just the Beginning of ldquoSecurity Gamesrdquohellip
Our next steps
59
Thank you
to sponsors
StartupARMORWAY Game theory in the field
Follow on research amp applications
Privacy audits
(Sinha IJCAIrsquo13)
Software testing
(Kukreja ASErsquo13)
Sport event security
(Yin AAAIrsquo14)
Singapore train
(Varakantham IAAIrsquo13)
Exam questions
(Li IJCAIrsquo13)
59
THANK YOU
tambeuscedu
httpteamcoreuscedusecurity
60
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
31
Patrols protect nearby ferry location Solve as done in ARMOR
Ferries Patrol RoutesExponential Numbers of Patrol Routes
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Patrols protect nearby ferry location Solve as done in ARMOR
Ferries Patrol RoutesExponential Numbers of Patrol Routes
32
Pr([(B5) (C 10) (C15)]) = 017
Pr([(B5) (C10) (B15)]) =013
NT variables
Pr([(A5) (A10) (B15)]) = 007
Pr([(A5) (A10) (A15)]) = 003
Jiang KiekintveldFang
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Variables NOT routes but probability flow over each segment
Ferries Scale-up Marginal Probabilities Over Segments
33
NT variables
Jiang KiekintveldFang
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
030
017
013
Obtain marginal probabilities over segments
010
007
003
N2T variablesExtract Pr([(B5) (C 10) (C15)]) = 017
Pr([(B5) (C10) (B15)]) =013
Ferries Scale-up with Marginals Over Separable SegmentsSignificant Speedup
34
NT variables
59
5936
Outline ldquoSecurity Gamesrdquo Research (2007-Now)
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
Evaluation II Real-world deployments
(Patience)
Evaluation I Scale up Handle uncertainty
59
TRUSTS Frequent adversary interaction games
Patrols Against Fare Evaders
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
017
013
010
010
37
59
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
38
Jiang Delle Fave
59
Markov Decision Problems in Security games
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
39
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution Jiang Delle Fave
5940
Urban Transportation Security
MDPs amp DEC-MDPs in Security Games
COPS LA Metro System(Against Opportunistic Crime)
STREETS Singapore Roads(Against Reckless Driving)
DEC-MDPs in Security Games Teamwork + Uncertainty
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
ZhangShieh
59
Uncertainty Space Algorithms
Bayesian and Robust Approaches
Adversary payoff uncertainty
Adversary observation amp
defender execution
uncertainty
Adversary rationality
uncertainty
RECON
BRASS
Monotonic Maximin
(Monotonic adversary)
41
URAC
Payoff interval
Not point
estimateGMC HUNTER
Bayesian
Robust
Yin Nguyen An
0
05
1
15
5 10
Def
end
ers
EU
Targets
ISG RECON URAC
59
Security Games Environmental Crime
amp Bounded Rationality
FisheryGulf of Mexico
Nakai Nam Theun
Forest Area Laos
WildlifeQueen Elizabeth National
ParkUganda
No patrols
Higher
density
Lower densityx
Yang Ford Brown
59
Uncertainty in Adversary Decision Bounded Rationality
Human Subjects as Poachers
59
Uncertainty in Adversary Decision[2009]
Human subjects Anchoring e-Optimality
-4
-3
-2
-1
0
1
Unobserved 5 Observations 20 Observations Unlimited
Average expected reward
DOBSS
Uniform
COBRA
ARMOR
ARMOR Outperforms uniform random
COBRA
MqxCaq
Xxxts
qxR
ji
Xi
ijj
Xi Qj
jiijqx
)1()()1(
|)|1()1(
max
ee
Anchoring
e-optimality
With Sarit Kraus 44
Pita
5945
Quantal Response Stochastic choice better choice more likely
Quantal Response Model of Adversary [2011] Not Maximize Expected Utility
T
j
jxadversary
EU
jxadversary
EU
j
e
eq
1
))((
))((
-3
-25
-2
-15
-1
-05
0
05
1
15
2
Payoff 1 Payoff 2 Payoff 3 Payoff 4
QuantalResponse
COBRA
ARMOR
Adversaryrsquos probability
of choosing target j
Yang
10
)(max1
1
)(
)(
t tt
defendT
jT
j
jxEU
jxEU
x
xKxts
jxEU
e
e
adversary
adversary
59
Uncertainty in Adversary Decision [2012]
Robust vs Modeling Adversaries
Robustness Bound loss to defender Not model attacker via QR
Defeating Robust Learned subjective utility
Robust wins Draw QR wins
42 52 6
Results on 100 games
SU-QR wins Draw Robust
13 8 1
Results on 22 games
MqxCa
qxR
ji
Xi
ij
jiij
Xi Qj
qx
)1()(0
max
β (Adversaryrsquos utility loss if
deviates from optimal) gt=
(Defenderrsquos utility loss due to
adversary deviation)
penaltyattackw
rewardattackw
probcapturewjaSEU
3
2
1)(
M
j
jxSEU
jxSEU
jadversary
adversary
e
eq
1
)(
)(
SU-QR wins Draw Robust
6 13 3
Results against security experts
With Sarit Kraus 46
5947
PAWS Protection Assistant for Wildlife Security[2014]Repeated Stackelberg Game
Simulation
Learn from crime data
Improve model
Defender calculates
mixed strategy
Defender executes
randomized patrols
Poachers attack targets
119890119878119864119880119894(w1w2w3)
119896 119890119878119864119880119896(w1w2w3)
Bayesian SUQR Heterogeneous Poachers
5948
PAWS Test April 2014
Trials in Queen Elizabeth National Park
Andrew Lemieux with rangers
on PAWS patrol in Uganda
59
Security Resource OptimizationEvaluating Deployed Security Systems Not Easy
Game theory Improvement over previous approaches
Previous Human schedulers or ldquosimple randomrdquo
49
Lab
Evaluation
Simulated
adversary
Human subject
adversaries
Field Evaluation
Patrol quality
Unpredictable Cover
Compare real schedules
Scheduling competition
Expert evaluation
Field Evaluation
Tests against adversaries
ldquoMock attackersrdquo
Capture rates of
real adversaries
59
Why Does Game Theory Perform Better
Weaknesses of Previous Methods
Human schedulers
Predictable patterns eg US Coast Guard
Scheduling effort amp cognitive burden
Simple random (eg dice roll)
Wrong weightscoverage eg officers to sparsely crowded terminals
No adversary reactions
Multiple deployments over multiple years without us forcing them
50
59
Lab Evaluation via Simulations
Example from IRIS (FAMS)
-10
-8
-6
-4
-2
0
2
4
6
50 150 250
Defe
nde
r E
xpe
cte
d u
tilit
y
Schedule Size
Uniform Weighted random 1 Weighted random 2 IRIS
51
59
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage
Patrols Before PROTECT Boston Patrols After PROTECT Boston
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Base Patrol Area
52
PROTECT (Coast Guard) 350 increase defender expected utility
59
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage for Less Effort
53
IRIS for FAMS Outperformed expert human over six monthsReportGAO-09-903T
ARMOR at LAX Savings of up to an hour a day in scheduling
59
Field Evaluation Human vs Game Theory Competition
Counter-terrorism Patrol Scheduling
90 officers on LA Metro Trains
Humans required significant effort
Worse schedules than game theory
Observerrsquos report on questions
54
3
35
4
45
5
55
Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Q9
Q10
Q11
Q12
Secu
rity
Sco
re
Human Game Theory
59
Field Test Against Adversaries Mock Attackers
Example from PROTECT
ldquoMock attackerrdquo team deployed in Boston
Comparing PRE- to POST-PROTECT ldquodeterrencerdquo improved
Additional real-world indicators from Boston
Boston boaters questions
ldquohas the Coast Guard recently acquired more boatsrdquo
POST-PROTECT Actual reports of illegal activity
55
5956
Field Tests Against Adversaries
Computational Game Theory in the Field
Game theory vs Random
21 days of patrol
Identical conditions
Random + Human
Not controlled
0
20
40
60
80
100 Miscellaneous
Drugs
Firearm Violations
0
5
10
15
20
Captures30 min
Warnings30 min
Violations30 min
Game Theory
Rand+Human
Controlled
59
Expert Evaluation
Example from ARMOR IRIS amp PROTECT
February 2009 Commendations
LAX Police (City of Los Angeles)
July 2011 Operational Excellence
Award (US Coast Guard Boston)
September 2011 Certificate of
Appreciation (Federal Air Marshals)
June 2013 Meritorious Team Commendation
from Commandant (US Coast Guard)
57
59
Summary Security Games
Decision aids based on computational game theory in daily use
Optimize limited security resources against adversaries
Applications yield research challenges Science of security games
Scale-up Incremental strategy generation amp Marginals
Uncertainty Integrate MDPs Robustness Quantal response
Current applications (wildlife security) Interdisciplinar challenge
Global challenges Merge planninglearning amp security games
58
59
Just the Beginning of ldquoSecurity Gamesrdquohellip
Our next steps
59
Thank you
to sponsors
StartupARMORWAY Game theory in the field
Follow on research amp applications
Privacy audits
(Sinha IJCAIrsquo13)
Software testing
(Kukreja ASErsquo13)
Sport event security
(Yin AAAIrsquo14)
Singapore train
(Varakantham IAAIrsquo13)
Exam questions
(Li IJCAIrsquo13)
59
THANK YOU
tambeuscedu
httpteamcoreuscedusecurity
60
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Patrols protect nearby ferry location Solve as done in ARMOR
Ferries Patrol RoutesExponential Numbers of Patrol Routes
32
Pr([(B5) (C 10) (C15)]) = 017
Pr([(B5) (C10) (B15)]) =013
NT variables
Pr([(A5) (A10) (B15)]) = 007
Pr([(A5) (A10) (A15)]) = 003
Jiang KiekintveldFang
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Variables NOT routes but probability flow over each segment
Ferries Scale-up Marginal Probabilities Over Segments
33
NT variables
Jiang KiekintveldFang
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
030
017
013
Obtain marginal probabilities over segments
010
007
003
N2T variablesExtract Pr([(B5) (C 10) (C15)]) = 017
Pr([(B5) (C10) (B15)]) =013
Ferries Scale-up with Marginals Over Separable SegmentsSignificant Speedup
34
NT variables
59
5936
Outline ldquoSecurity Gamesrdquo Research (2007-Now)
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
Evaluation II Real-world deployments
(Patience)
Evaluation I Scale up Handle uncertainty
59
TRUSTS Frequent adversary interaction games
Patrols Against Fare Evaders
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
017
013
010
010
37
59
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
38
Jiang Delle Fave
59
Markov Decision Problems in Security games
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
39
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution Jiang Delle Fave
5940
Urban Transportation Security
MDPs amp DEC-MDPs in Security Games
COPS LA Metro System(Against Opportunistic Crime)
STREETS Singapore Roads(Against Reckless Driving)
DEC-MDPs in Security Games Teamwork + Uncertainty
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
ZhangShieh
59
Uncertainty Space Algorithms
Bayesian and Robust Approaches
Adversary payoff uncertainty
Adversary observation amp
defender execution
uncertainty
Adversary rationality
uncertainty
RECON
BRASS
Monotonic Maximin
(Monotonic adversary)
41
URAC
Payoff interval
Not point
estimateGMC HUNTER
Bayesian
Robust
Yin Nguyen An
0
05
1
15
5 10
Def
end
ers
EU
Targets
ISG RECON URAC
59
Security Games Environmental Crime
amp Bounded Rationality
FisheryGulf of Mexico
Nakai Nam Theun
Forest Area Laos
WildlifeQueen Elizabeth National
ParkUganda
No patrols
Higher
density
Lower densityx
Yang Ford Brown
59
Uncertainty in Adversary Decision Bounded Rationality
Human Subjects as Poachers
59
Uncertainty in Adversary Decision[2009]
Human subjects Anchoring e-Optimality
-4
-3
-2
-1
0
1
Unobserved 5 Observations 20 Observations Unlimited
Average expected reward
DOBSS
Uniform
COBRA
ARMOR
ARMOR Outperforms uniform random
COBRA
MqxCaq
Xxxts
qxR
ji
Xi
ijj
Xi Qj
jiijqx
)1()()1(
|)|1()1(
max
ee
Anchoring
e-optimality
With Sarit Kraus 44
Pita
5945
Quantal Response Stochastic choice better choice more likely
Quantal Response Model of Adversary [2011] Not Maximize Expected Utility
T
j
jxadversary
EU
jxadversary
EU
j
e
eq
1
))((
))((
-3
-25
-2
-15
-1
-05
0
05
1
15
2
Payoff 1 Payoff 2 Payoff 3 Payoff 4
QuantalResponse
COBRA
ARMOR
Adversaryrsquos probability
of choosing target j
Yang
10
)(max1
1
)(
)(
t tt
defendT
jT
j
jxEU
jxEU
x
xKxts
jxEU
e
e
adversary
adversary
59
Uncertainty in Adversary Decision [2012]
Robust vs Modeling Adversaries
Robustness Bound loss to defender Not model attacker via QR
Defeating Robust Learned subjective utility
Robust wins Draw QR wins
42 52 6
Results on 100 games
SU-QR wins Draw Robust
13 8 1
Results on 22 games
MqxCa
qxR
ji
Xi
ij
jiij
Xi Qj
qx
)1()(0
max
β (Adversaryrsquos utility loss if
deviates from optimal) gt=
(Defenderrsquos utility loss due to
adversary deviation)
penaltyattackw
rewardattackw
probcapturewjaSEU
3
2
1)(
M
j
jxSEU
jxSEU
jadversary
adversary
e
eq
1
)(
)(
SU-QR wins Draw Robust
6 13 3
Results against security experts
With Sarit Kraus 46
5947
PAWS Protection Assistant for Wildlife Security[2014]Repeated Stackelberg Game
Simulation
Learn from crime data
Improve model
Defender calculates
mixed strategy
Defender executes
randomized patrols
Poachers attack targets
119890119878119864119880119894(w1w2w3)
119896 119890119878119864119880119896(w1w2w3)
Bayesian SUQR Heterogeneous Poachers
5948
PAWS Test April 2014
Trials in Queen Elizabeth National Park
Andrew Lemieux with rangers
on PAWS patrol in Uganda
59
Security Resource OptimizationEvaluating Deployed Security Systems Not Easy
Game theory Improvement over previous approaches
Previous Human schedulers or ldquosimple randomrdquo
49
Lab
Evaluation
Simulated
adversary
Human subject
adversaries
Field Evaluation
Patrol quality
Unpredictable Cover
Compare real schedules
Scheduling competition
Expert evaluation
Field Evaluation
Tests against adversaries
ldquoMock attackersrdquo
Capture rates of
real adversaries
59
Why Does Game Theory Perform Better
Weaknesses of Previous Methods
Human schedulers
Predictable patterns eg US Coast Guard
Scheduling effort amp cognitive burden
Simple random (eg dice roll)
Wrong weightscoverage eg officers to sparsely crowded terminals
No adversary reactions
Multiple deployments over multiple years without us forcing them
50
59
Lab Evaluation via Simulations
Example from IRIS (FAMS)
-10
-8
-6
-4
-2
0
2
4
6
50 150 250
Defe
nde
r E
xpe
cte
d u
tilit
y
Schedule Size
Uniform Weighted random 1 Weighted random 2 IRIS
51
59
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage
Patrols Before PROTECT Boston Patrols After PROTECT Boston
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Base Patrol Area
52
PROTECT (Coast Guard) 350 increase defender expected utility
59
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage for Less Effort
53
IRIS for FAMS Outperformed expert human over six monthsReportGAO-09-903T
ARMOR at LAX Savings of up to an hour a day in scheduling
59
Field Evaluation Human vs Game Theory Competition
Counter-terrorism Patrol Scheduling
90 officers on LA Metro Trains
Humans required significant effort
Worse schedules than game theory
Observerrsquos report on questions
54
3
35
4
45
5
55
Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Q9
Q10
Q11
Q12
Secu
rity
Sco
re
Human Game Theory
59
Field Test Against Adversaries Mock Attackers
Example from PROTECT
ldquoMock attackerrdquo team deployed in Boston
Comparing PRE- to POST-PROTECT ldquodeterrencerdquo improved
Additional real-world indicators from Boston
Boston boaters questions
ldquohas the Coast Guard recently acquired more boatsrdquo
POST-PROTECT Actual reports of illegal activity
55
5956
Field Tests Against Adversaries
Computational Game Theory in the Field
Game theory vs Random
21 days of patrol
Identical conditions
Random + Human
Not controlled
0
20
40
60
80
100 Miscellaneous
Drugs
Firearm Violations
0
5
10
15
20
Captures30 min
Warnings30 min
Violations30 min
Game Theory
Rand+Human
Controlled
59
Expert Evaluation
Example from ARMOR IRIS amp PROTECT
February 2009 Commendations
LAX Police (City of Los Angeles)
July 2011 Operational Excellence
Award (US Coast Guard Boston)
September 2011 Certificate of
Appreciation (Federal Air Marshals)
June 2013 Meritorious Team Commendation
from Commandant (US Coast Guard)
57
59
Summary Security Games
Decision aids based on computational game theory in daily use
Optimize limited security resources against adversaries
Applications yield research challenges Science of security games
Scale-up Incremental strategy generation amp Marginals
Uncertainty Integrate MDPs Robustness Quantal response
Current applications (wildlife security) Interdisciplinar challenge
Global challenges Merge planninglearning amp security games
58
59
Just the Beginning of ldquoSecurity Gamesrdquohellip
Our next steps
59
Thank you
to sponsors
StartupARMORWAY Game theory in the field
Follow on research amp applications
Privacy audits
(Sinha IJCAIrsquo13)
Software testing
(Kukreja ASErsquo13)
Sport event security
(Yin AAAIrsquo14)
Singapore train
(Varakantham IAAIrsquo13)
Exam questions
(Li IJCAIrsquo13)
59
THANK YOU
tambeuscedu
httpteamcoreuscedusecurity
60
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
Variables NOT routes but probability flow over each segment
Ferries Scale-up Marginal Probabilities Over Segments
33
NT variables
Jiang KiekintveldFang
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
030
017
013
Obtain marginal probabilities over segments
010
007
003
N2T variablesExtract Pr([(B5) (C 10) (C15)]) = 017
Pr([(B5) (C10) (B15)]) =013
Ferries Scale-up with Marginals Over Separable SegmentsSignificant Speedup
34
NT variables
59
5936
Outline ldquoSecurity Gamesrdquo Research (2007-Now)
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
Evaluation II Real-world deployments
(Patience)
Evaluation I Scale up Handle uncertainty
59
TRUSTS Frequent adversary interaction games
Patrols Against Fare Evaders
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
017
013
010
010
37
59
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
38
Jiang Delle Fave
59
Markov Decision Problems in Security games
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
39
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution Jiang Delle Fave
5940
Urban Transportation Security
MDPs amp DEC-MDPs in Security Games
COPS LA Metro System(Against Opportunistic Crime)
STREETS Singapore Roads(Against Reckless Driving)
DEC-MDPs in Security Games Teamwork + Uncertainty
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
ZhangShieh
59
Uncertainty Space Algorithms
Bayesian and Robust Approaches
Adversary payoff uncertainty
Adversary observation amp
defender execution
uncertainty
Adversary rationality
uncertainty
RECON
BRASS
Monotonic Maximin
(Monotonic adversary)
41
URAC
Payoff interval
Not point
estimateGMC HUNTER
Bayesian
Robust
Yin Nguyen An
0
05
1
15
5 10
Def
end
ers
EU
Targets
ISG RECON URAC
59
Security Games Environmental Crime
amp Bounded Rationality
FisheryGulf of Mexico
Nakai Nam Theun
Forest Area Laos
WildlifeQueen Elizabeth National
ParkUganda
No patrols
Higher
density
Lower densityx
Yang Ford Brown
59
Uncertainty in Adversary Decision Bounded Rationality
Human Subjects as Poachers
59
Uncertainty in Adversary Decision[2009]
Human subjects Anchoring e-Optimality
-4
-3
-2
-1
0
1
Unobserved 5 Observations 20 Observations Unlimited
Average expected reward
DOBSS
Uniform
COBRA
ARMOR
ARMOR Outperforms uniform random
COBRA
MqxCaq
Xxxts
qxR
ji
Xi
ijj
Xi Qj
jiijqx
)1()()1(
|)|1()1(
max
ee
Anchoring
e-optimality
With Sarit Kraus 44
Pita
5945
Quantal Response Stochastic choice better choice more likely
Quantal Response Model of Adversary [2011] Not Maximize Expected Utility
T
j
jxadversary
EU
jxadversary
EU
j
e
eq
1
))((
))((
-3
-25
-2
-15
-1
-05
0
05
1
15
2
Payoff 1 Payoff 2 Payoff 3 Payoff 4
QuantalResponse
COBRA
ARMOR
Adversaryrsquos probability
of choosing target j
Yang
10
)(max1
1
)(
)(
t tt
defendT
jT
j
jxEU
jxEU
x
xKxts
jxEU
e
e
adversary
adversary
59
Uncertainty in Adversary Decision [2012]
Robust vs Modeling Adversaries
Robustness Bound loss to defender Not model attacker via QR
Defeating Robust Learned subjective utility
Robust wins Draw QR wins
42 52 6
Results on 100 games
SU-QR wins Draw Robust
13 8 1
Results on 22 games
MqxCa
qxR
ji
Xi
ij
jiij
Xi Qj
qx
)1()(0
max
β (Adversaryrsquos utility loss if
deviates from optimal) gt=
(Defenderrsquos utility loss due to
adversary deviation)
penaltyattackw
rewardattackw
probcapturewjaSEU
3
2
1)(
M
j
jxSEU
jxSEU
jadversary
adversary
e
eq
1
)(
)(
SU-QR wins Draw Robust
6 13 3
Results against security experts
With Sarit Kraus 46
5947
PAWS Protection Assistant for Wildlife Security[2014]Repeated Stackelberg Game
Simulation
Learn from crime data
Improve model
Defender calculates
mixed strategy
Defender executes
randomized patrols
Poachers attack targets
119890119878119864119880119894(w1w2w3)
119896 119890119878119864119880119896(w1w2w3)
Bayesian SUQR Heterogeneous Poachers
5948
PAWS Test April 2014
Trials in Queen Elizabeth National Park
Andrew Lemieux with rangers
on PAWS patrol in Uganda
59
Security Resource OptimizationEvaluating Deployed Security Systems Not Easy
Game theory Improvement over previous approaches
Previous Human schedulers or ldquosimple randomrdquo
49
Lab
Evaluation
Simulated
adversary
Human subject
adversaries
Field Evaluation
Patrol quality
Unpredictable Cover
Compare real schedules
Scheduling competition
Expert evaluation
Field Evaluation
Tests against adversaries
ldquoMock attackersrdquo
Capture rates of
real adversaries
59
Why Does Game Theory Perform Better
Weaknesses of Previous Methods
Human schedulers
Predictable patterns eg US Coast Guard
Scheduling effort amp cognitive burden
Simple random (eg dice roll)
Wrong weightscoverage eg officers to sparsely crowded terminals
No adversary reactions
Multiple deployments over multiple years without us forcing them
50
59
Lab Evaluation via Simulations
Example from IRIS (FAMS)
-10
-8
-6
-4
-2
0
2
4
6
50 150 250
Defe
nde
r E
xpe
cte
d u
tilit
y
Schedule Size
Uniform Weighted random 1 Weighted random 2 IRIS
51
59
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage
Patrols Before PROTECT Boston Patrols After PROTECT Boston
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Base Patrol Area
52
PROTECT (Coast Guard) 350 increase defender expected utility
59
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage for Less Effort
53
IRIS for FAMS Outperformed expert human over six monthsReportGAO-09-903T
ARMOR at LAX Savings of up to an hour a day in scheduling
59
Field Evaluation Human vs Game Theory Competition
Counter-terrorism Patrol Scheduling
90 officers on LA Metro Trains
Humans required significant effort
Worse schedules than game theory
Observerrsquos report on questions
54
3
35
4
45
5
55
Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Q9
Q10
Q11
Q12
Secu
rity
Sco
re
Human Game Theory
59
Field Test Against Adversaries Mock Attackers
Example from PROTECT
ldquoMock attackerrdquo team deployed in Boston
Comparing PRE- to POST-PROTECT ldquodeterrencerdquo improved
Additional real-world indicators from Boston
Boston boaters questions
ldquohas the Coast Guard recently acquired more boatsrdquo
POST-PROTECT Actual reports of illegal activity
55
5956
Field Tests Against Adversaries
Computational Game Theory in the Field
Game theory vs Random
21 days of patrol
Identical conditions
Random + Human
Not controlled
0
20
40
60
80
100 Miscellaneous
Drugs
Firearm Violations
0
5
10
15
20
Captures30 min
Warnings30 min
Violations30 min
Game Theory
Rand+Human
Controlled
59
Expert Evaluation
Example from ARMOR IRIS amp PROTECT
February 2009 Commendations
LAX Police (City of Los Angeles)
July 2011 Operational Excellence
Award (US Coast Guard Boston)
September 2011 Certificate of
Appreciation (Federal Air Marshals)
June 2013 Meritorious Team Commendation
from Commandant (US Coast Guard)
57
59
Summary Security Games
Decision aids based on computational game theory in daily use
Optimize limited security resources against adversaries
Applications yield research challenges Science of security games
Scale-up Incremental strategy generation amp Marginals
Uncertainty Integrate MDPs Robustness Quantal response
Current applications (wildlife security) Interdisciplinar challenge
Global challenges Merge planninglearning amp security games
58
59
Just the Beginning of ldquoSecurity Gamesrdquohellip
Our next steps
59
Thank you
to sponsors
StartupARMORWAY Game theory in the field
Follow on research amp applications
Privacy audits
(Sinha IJCAIrsquo13)
Software testing
(Kukreja ASErsquo13)
Sport event security
(Yin AAAIrsquo14)
Singapore train
(Varakantham IAAIrsquo13)
Exam questions
(Li IJCAIrsquo13)
59
THANK YOU
tambeuscedu
httpteamcoreuscedusecurity
60
59
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
Ferry
030
017
013
Obtain marginal probabilities over segments
010
007
003
N2T variablesExtract Pr([(B5) (C 10) (C15)]) = 017
Pr([(B5) (C10) (B15)]) =013
Ferries Scale-up with Marginals Over Separable SegmentsSignificant Speedup
34
NT variables
59
5936
Outline ldquoSecurity Gamesrdquo Research (2007-Now)
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
Evaluation II Real-world deployments
(Patience)
Evaluation I Scale up Handle uncertainty
59
TRUSTS Frequent adversary interaction games
Patrols Against Fare Evaders
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
017
013
010
010
37
59
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
38
Jiang Delle Fave
59
Markov Decision Problems in Security games
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
39
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution Jiang Delle Fave
5940
Urban Transportation Security
MDPs amp DEC-MDPs in Security Games
COPS LA Metro System(Against Opportunistic Crime)
STREETS Singapore Roads(Against Reckless Driving)
DEC-MDPs in Security Games Teamwork + Uncertainty
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
ZhangShieh
59
Uncertainty Space Algorithms
Bayesian and Robust Approaches
Adversary payoff uncertainty
Adversary observation amp
defender execution
uncertainty
Adversary rationality
uncertainty
RECON
BRASS
Monotonic Maximin
(Monotonic adversary)
41
URAC
Payoff interval
Not point
estimateGMC HUNTER
Bayesian
Robust
Yin Nguyen An
0
05
1
15
5 10
Def
end
ers
EU
Targets
ISG RECON URAC
59
Security Games Environmental Crime
amp Bounded Rationality
FisheryGulf of Mexico
Nakai Nam Theun
Forest Area Laos
WildlifeQueen Elizabeth National
ParkUganda
No patrols
Higher
density
Lower densityx
Yang Ford Brown
59
Uncertainty in Adversary Decision Bounded Rationality
Human Subjects as Poachers
59
Uncertainty in Adversary Decision[2009]
Human subjects Anchoring e-Optimality
-4
-3
-2
-1
0
1
Unobserved 5 Observations 20 Observations Unlimited
Average expected reward
DOBSS
Uniform
COBRA
ARMOR
ARMOR Outperforms uniform random
COBRA
MqxCaq
Xxxts
qxR
ji
Xi
ijj
Xi Qj
jiijqx
)1()()1(
|)|1()1(
max
ee
Anchoring
e-optimality
With Sarit Kraus 44
Pita
5945
Quantal Response Stochastic choice better choice more likely
Quantal Response Model of Adversary [2011] Not Maximize Expected Utility
T
j
jxadversary
EU
jxadversary
EU
j
e
eq
1
))((
))((
-3
-25
-2
-15
-1
-05
0
05
1
15
2
Payoff 1 Payoff 2 Payoff 3 Payoff 4
QuantalResponse
COBRA
ARMOR
Adversaryrsquos probability
of choosing target j
Yang
10
)(max1
1
)(
)(
t tt
defendT
jT
j
jxEU
jxEU
x
xKxts
jxEU
e
e
adversary
adversary
59
Uncertainty in Adversary Decision [2012]
Robust vs Modeling Adversaries
Robustness Bound loss to defender Not model attacker via QR
Defeating Robust Learned subjective utility
Robust wins Draw QR wins
42 52 6
Results on 100 games
SU-QR wins Draw Robust
13 8 1
Results on 22 games
MqxCa
qxR
ji
Xi
ij
jiij
Xi Qj
qx
)1()(0
max
β (Adversaryrsquos utility loss if
deviates from optimal) gt=
(Defenderrsquos utility loss due to
adversary deviation)
penaltyattackw
rewardattackw
probcapturewjaSEU
3
2
1)(
M
j
jxSEU
jxSEU
jadversary
adversary
e
eq
1
)(
)(
SU-QR wins Draw Robust
6 13 3
Results against security experts
With Sarit Kraus 46
5947
PAWS Protection Assistant for Wildlife Security[2014]Repeated Stackelberg Game
Simulation
Learn from crime data
Improve model
Defender calculates
mixed strategy
Defender executes
randomized patrols
Poachers attack targets
119890119878119864119880119894(w1w2w3)
119896 119890119878119864119880119896(w1w2w3)
Bayesian SUQR Heterogeneous Poachers
5948
PAWS Test April 2014
Trials in Queen Elizabeth National Park
Andrew Lemieux with rangers
on PAWS patrol in Uganda
59
Security Resource OptimizationEvaluating Deployed Security Systems Not Easy
Game theory Improvement over previous approaches
Previous Human schedulers or ldquosimple randomrdquo
49
Lab
Evaluation
Simulated
adversary
Human subject
adversaries
Field Evaluation
Patrol quality
Unpredictable Cover
Compare real schedules
Scheduling competition
Expert evaluation
Field Evaluation
Tests against adversaries
ldquoMock attackersrdquo
Capture rates of
real adversaries
59
Why Does Game Theory Perform Better
Weaknesses of Previous Methods
Human schedulers
Predictable patterns eg US Coast Guard
Scheduling effort amp cognitive burden
Simple random (eg dice roll)
Wrong weightscoverage eg officers to sparsely crowded terminals
No adversary reactions
Multiple deployments over multiple years without us forcing them
50
59
Lab Evaluation via Simulations
Example from IRIS (FAMS)
-10
-8
-6
-4
-2
0
2
4
6
50 150 250
Defe
nde
r E
xpe
cte
d u
tilit
y
Schedule Size
Uniform Weighted random 1 Weighted random 2 IRIS
51
59
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage
Patrols Before PROTECT Boston Patrols After PROTECT Boston
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Base Patrol Area
52
PROTECT (Coast Guard) 350 increase defender expected utility
59
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage for Less Effort
53
IRIS for FAMS Outperformed expert human over six monthsReportGAO-09-903T
ARMOR at LAX Savings of up to an hour a day in scheduling
59
Field Evaluation Human vs Game Theory Competition
Counter-terrorism Patrol Scheduling
90 officers on LA Metro Trains
Humans required significant effort
Worse schedules than game theory
Observerrsquos report on questions
54
3
35
4
45
5
55
Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Q9
Q10
Q11
Q12
Secu
rity
Sco
re
Human Game Theory
59
Field Test Against Adversaries Mock Attackers
Example from PROTECT
ldquoMock attackerrdquo team deployed in Boston
Comparing PRE- to POST-PROTECT ldquodeterrencerdquo improved
Additional real-world indicators from Boston
Boston boaters questions
ldquohas the Coast Guard recently acquired more boatsrdquo
POST-PROTECT Actual reports of illegal activity
55
5956
Field Tests Against Adversaries
Computational Game Theory in the Field
Game theory vs Random
21 days of patrol
Identical conditions
Random + Human
Not controlled
0
20
40
60
80
100 Miscellaneous
Drugs
Firearm Violations
0
5
10
15
20
Captures30 min
Warnings30 min
Violations30 min
Game Theory
Rand+Human
Controlled
59
Expert Evaluation
Example from ARMOR IRIS amp PROTECT
February 2009 Commendations
LAX Police (City of Los Angeles)
July 2011 Operational Excellence
Award (US Coast Guard Boston)
September 2011 Certificate of
Appreciation (Federal Air Marshals)
June 2013 Meritorious Team Commendation
from Commandant (US Coast Guard)
57
59
Summary Security Games
Decision aids based on computational game theory in daily use
Optimize limited security resources against adversaries
Applications yield research challenges Science of security games
Scale-up Incremental strategy generation amp Marginals
Uncertainty Integrate MDPs Robustness Quantal response
Current applications (wildlife security) Interdisciplinar challenge
Global challenges Merge planninglearning amp security games
58
59
Just the Beginning of ldquoSecurity Gamesrdquohellip
Our next steps
59
Thank you
to sponsors
StartupARMORWAY Game theory in the field
Follow on research amp applications
Privacy audits
(Sinha IJCAIrsquo13)
Software testing
(Kukreja ASErsquo13)
Sport event security
(Yin AAAIrsquo14)
Singapore train
(Varakantham IAAIrsquo13)
Exam questions
(Li IJCAIrsquo13)
59
THANK YOU
tambeuscedu
httpteamcoreuscedusecurity
60
59
5936
Outline ldquoSecurity Gamesrdquo Research (2007-Now)
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
Evaluation II Real-world deployments
(Patience)
Evaluation I Scale up Handle uncertainty
59
TRUSTS Frequent adversary interaction games
Patrols Against Fare Evaders
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
017
013
010
010
37
59
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
38
Jiang Delle Fave
59
Markov Decision Problems in Security games
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
39
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution Jiang Delle Fave
5940
Urban Transportation Security
MDPs amp DEC-MDPs in Security Games
COPS LA Metro System(Against Opportunistic Crime)
STREETS Singapore Roads(Against Reckless Driving)
DEC-MDPs in Security Games Teamwork + Uncertainty
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
ZhangShieh
59
Uncertainty Space Algorithms
Bayesian and Robust Approaches
Adversary payoff uncertainty
Adversary observation amp
defender execution
uncertainty
Adversary rationality
uncertainty
RECON
BRASS
Monotonic Maximin
(Monotonic adversary)
41
URAC
Payoff interval
Not point
estimateGMC HUNTER
Bayesian
Robust
Yin Nguyen An
0
05
1
15
5 10
Def
end
ers
EU
Targets
ISG RECON URAC
59
Security Games Environmental Crime
amp Bounded Rationality
FisheryGulf of Mexico
Nakai Nam Theun
Forest Area Laos
WildlifeQueen Elizabeth National
ParkUganda
No patrols
Higher
density
Lower densityx
Yang Ford Brown
59
Uncertainty in Adversary Decision Bounded Rationality
Human Subjects as Poachers
59
Uncertainty in Adversary Decision[2009]
Human subjects Anchoring e-Optimality
-4
-3
-2
-1
0
1
Unobserved 5 Observations 20 Observations Unlimited
Average expected reward
DOBSS
Uniform
COBRA
ARMOR
ARMOR Outperforms uniform random
COBRA
MqxCaq
Xxxts
qxR
ji
Xi
ijj
Xi Qj
jiijqx
)1()()1(
|)|1()1(
max
ee
Anchoring
e-optimality
With Sarit Kraus 44
Pita
5945
Quantal Response Stochastic choice better choice more likely
Quantal Response Model of Adversary [2011] Not Maximize Expected Utility
T
j
jxadversary
EU
jxadversary
EU
j
e
eq
1
))((
))((
-3
-25
-2
-15
-1
-05
0
05
1
15
2
Payoff 1 Payoff 2 Payoff 3 Payoff 4
QuantalResponse
COBRA
ARMOR
Adversaryrsquos probability
of choosing target j
Yang
10
)(max1
1
)(
)(
t tt
defendT
jT
j
jxEU
jxEU
x
xKxts
jxEU
e
e
adversary
adversary
59
Uncertainty in Adversary Decision [2012]
Robust vs Modeling Adversaries
Robustness Bound loss to defender Not model attacker via QR
Defeating Robust Learned subjective utility
Robust wins Draw QR wins
42 52 6
Results on 100 games
SU-QR wins Draw Robust
13 8 1
Results on 22 games
MqxCa
qxR
ji
Xi
ij
jiij
Xi Qj
qx
)1()(0
max
β (Adversaryrsquos utility loss if
deviates from optimal) gt=
(Defenderrsquos utility loss due to
adversary deviation)
penaltyattackw
rewardattackw
probcapturewjaSEU
3
2
1)(
M
j
jxSEU
jxSEU
jadversary
adversary
e
eq
1
)(
)(
SU-QR wins Draw Robust
6 13 3
Results against security experts
With Sarit Kraus 46
5947
PAWS Protection Assistant for Wildlife Security[2014]Repeated Stackelberg Game
Simulation
Learn from crime data
Improve model
Defender calculates
mixed strategy
Defender executes
randomized patrols
Poachers attack targets
119890119878119864119880119894(w1w2w3)
119896 119890119878119864119880119896(w1w2w3)
Bayesian SUQR Heterogeneous Poachers
5948
PAWS Test April 2014
Trials in Queen Elizabeth National Park
Andrew Lemieux with rangers
on PAWS patrol in Uganda
59
Security Resource OptimizationEvaluating Deployed Security Systems Not Easy
Game theory Improvement over previous approaches
Previous Human schedulers or ldquosimple randomrdquo
49
Lab
Evaluation
Simulated
adversary
Human subject
adversaries
Field Evaluation
Patrol quality
Unpredictable Cover
Compare real schedules
Scheduling competition
Expert evaluation
Field Evaluation
Tests against adversaries
ldquoMock attackersrdquo
Capture rates of
real adversaries
59
Why Does Game Theory Perform Better
Weaknesses of Previous Methods
Human schedulers
Predictable patterns eg US Coast Guard
Scheduling effort amp cognitive burden
Simple random (eg dice roll)
Wrong weightscoverage eg officers to sparsely crowded terminals
No adversary reactions
Multiple deployments over multiple years without us forcing them
50
59
Lab Evaluation via Simulations
Example from IRIS (FAMS)
-10
-8
-6
-4
-2
0
2
4
6
50 150 250
Defe
nde
r E
xpe
cte
d u
tilit
y
Schedule Size
Uniform Weighted random 1 Weighted random 2 IRIS
51
59
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage
Patrols Before PROTECT Boston Patrols After PROTECT Boston
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Base Patrol Area
52
PROTECT (Coast Guard) 350 increase defender expected utility
59
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage for Less Effort
53
IRIS for FAMS Outperformed expert human over six monthsReportGAO-09-903T
ARMOR at LAX Savings of up to an hour a day in scheduling
59
Field Evaluation Human vs Game Theory Competition
Counter-terrorism Patrol Scheduling
90 officers on LA Metro Trains
Humans required significant effort
Worse schedules than game theory
Observerrsquos report on questions
54
3
35
4
45
5
55
Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Q9
Q10
Q11
Q12
Secu
rity
Sco
re
Human Game Theory
59
Field Test Against Adversaries Mock Attackers
Example from PROTECT
ldquoMock attackerrdquo team deployed in Boston
Comparing PRE- to POST-PROTECT ldquodeterrencerdquo improved
Additional real-world indicators from Boston
Boston boaters questions
ldquohas the Coast Guard recently acquired more boatsrdquo
POST-PROTECT Actual reports of illegal activity
55
5956
Field Tests Against Adversaries
Computational Game Theory in the Field
Game theory vs Random
21 days of patrol
Identical conditions
Random + Human
Not controlled
0
20
40
60
80
100 Miscellaneous
Drugs
Firearm Violations
0
5
10
15
20
Captures30 min
Warnings30 min
Violations30 min
Game Theory
Rand+Human
Controlled
59
Expert Evaluation
Example from ARMOR IRIS amp PROTECT
February 2009 Commendations
LAX Police (City of Los Angeles)
July 2011 Operational Excellence
Award (US Coast Guard Boston)
September 2011 Certificate of
Appreciation (Federal Air Marshals)
June 2013 Meritorious Team Commendation
from Commandant (US Coast Guard)
57
59
Summary Security Games
Decision aids based on computational game theory in daily use
Optimize limited security resources against adversaries
Applications yield research challenges Science of security games
Scale-up Incremental strategy generation amp Marginals
Uncertainty Integrate MDPs Robustness Quantal response
Current applications (wildlife security) Interdisciplinar challenge
Global challenges Merge planninglearning amp security games
58
59
Just the Beginning of ldquoSecurity Gamesrdquohellip
Our next steps
59
Thank you
to sponsors
StartupARMORWAY Game theory in the field
Follow on research amp applications
Privacy audits
(Sinha IJCAIrsquo13)
Software testing
(Kukreja ASErsquo13)
Sport event security
(Yin AAAIrsquo14)
Singapore train
(Varakantham IAAIrsquo13)
Exam questions
(Li IJCAIrsquo13)
59
THANK YOU
tambeuscedu
httpteamcoreuscedusecurity
60
5936
Outline ldquoSecurity Gamesrdquo Research (2007-Now)
2007 2009 2011 2012 2013 2013-
Airports
Flights
PortsRoads
TrainsEnvironment
Evaluation II Real-world deployments
(Patience)
Evaluation I Scale up Handle uncertainty
59
TRUSTS Frequent adversary interaction games
Patrols Against Fare Evaders
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
017
013
010
010
37
59
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
38
Jiang Delle Fave
59
Markov Decision Problems in Security games
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
39
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution Jiang Delle Fave
5940
Urban Transportation Security
MDPs amp DEC-MDPs in Security Games
COPS LA Metro System(Against Opportunistic Crime)
STREETS Singapore Roads(Against Reckless Driving)
DEC-MDPs in Security Games Teamwork + Uncertainty
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
ZhangShieh
59
Uncertainty Space Algorithms
Bayesian and Robust Approaches
Adversary payoff uncertainty
Adversary observation amp
defender execution
uncertainty
Adversary rationality
uncertainty
RECON
BRASS
Monotonic Maximin
(Monotonic adversary)
41
URAC
Payoff interval
Not point
estimateGMC HUNTER
Bayesian
Robust
Yin Nguyen An
0
05
1
15
5 10
Def
end
ers
EU
Targets
ISG RECON URAC
59
Security Games Environmental Crime
amp Bounded Rationality
FisheryGulf of Mexico
Nakai Nam Theun
Forest Area Laos
WildlifeQueen Elizabeth National
ParkUganda
No patrols
Higher
density
Lower densityx
Yang Ford Brown
59
Uncertainty in Adversary Decision Bounded Rationality
Human Subjects as Poachers
59
Uncertainty in Adversary Decision[2009]
Human subjects Anchoring e-Optimality
-4
-3
-2
-1
0
1
Unobserved 5 Observations 20 Observations Unlimited
Average expected reward
DOBSS
Uniform
COBRA
ARMOR
ARMOR Outperforms uniform random
COBRA
MqxCaq
Xxxts
qxR
ji
Xi
ijj
Xi Qj
jiijqx
)1()()1(
|)|1()1(
max
ee
Anchoring
e-optimality
With Sarit Kraus 44
Pita
5945
Quantal Response Stochastic choice better choice more likely
Quantal Response Model of Adversary [2011] Not Maximize Expected Utility
T
j
jxadversary
EU
jxadversary
EU
j
e
eq
1
))((
))((
-3
-25
-2
-15
-1
-05
0
05
1
15
2
Payoff 1 Payoff 2 Payoff 3 Payoff 4
QuantalResponse
COBRA
ARMOR
Adversaryrsquos probability
of choosing target j
Yang
10
)(max1
1
)(
)(
t tt
defendT
jT
j
jxEU
jxEU
x
xKxts
jxEU
e
e
adversary
adversary
59
Uncertainty in Adversary Decision [2012]
Robust vs Modeling Adversaries
Robustness Bound loss to defender Not model attacker via QR
Defeating Robust Learned subjective utility
Robust wins Draw QR wins
42 52 6
Results on 100 games
SU-QR wins Draw Robust
13 8 1
Results on 22 games
MqxCa
qxR
ji
Xi
ij
jiij
Xi Qj
qx
)1()(0
max
β (Adversaryrsquos utility loss if
deviates from optimal) gt=
(Defenderrsquos utility loss due to
adversary deviation)
penaltyattackw
rewardattackw
probcapturewjaSEU
3
2
1)(
M
j
jxSEU
jxSEU
jadversary
adversary
e
eq
1
)(
)(
SU-QR wins Draw Robust
6 13 3
Results against security experts
With Sarit Kraus 46
5947
PAWS Protection Assistant for Wildlife Security[2014]Repeated Stackelberg Game
Simulation
Learn from crime data
Improve model
Defender calculates
mixed strategy
Defender executes
randomized patrols
Poachers attack targets
119890119878119864119880119894(w1w2w3)
119896 119890119878119864119880119896(w1w2w3)
Bayesian SUQR Heterogeneous Poachers
5948
PAWS Test April 2014
Trials in Queen Elizabeth National Park
Andrew Lemieux with rangers
on PAWS patrol in Uganda
59
Security Resource OptimizationEvaluating Deployed Security Systems Not Easy
Game theory Improvement over previous approaches
Previous Human schedulers or ldquosimple randomrdquo
49
Lab
Evaluation
Simulated
adversary
Human subject
adversaries
Field Evaluation
Patrol quality
Unpredictable Cover
Compare real schedules
Scheduling competition
Expert evaluation
Field Evaluation
Tests against adversaries
ldquoMock attackersrdquo
Capture rates of
real adversaries
59
Why Does Game Theory Perform Better
Weaknesses of Previous Methods
Human schedulers
Predictable patterns eg US Coast Guard
Scheduling effort amp cognitive burden
Simple random (eg dice roll)
Wrong weightscoverage eg officers to sparsely crowded terminals
No adversary reactions
Multiple deployments over multiple years without us forcing them
50
59
Lab Evaluation via Simulations
Example from IRIS (FAMS)
-10
-8
-6
-4
-2
0
2
4
6
50 150 250
Defe
nde
r E
xpe
cte
d u
tilit
y
Schedule Size
Uniform Weighted random 1 Weighted random 2 IRIS
51
59
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage
Patrols Before PROTECT Boston Patrols After PROTECT Boston
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Base Patrol Area
52
PROTECT (Coast Guard) 350 increase defender expected utility
59
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage for Less Effort
53
IRIS for FAMS Outperformed expert human over six monthsReportGAO-09-903T
ARMOR at LAX Savings of up to an hour a day in scheduling
59
Field Evaluation Human vs Game Theory Competition
Counter-terrorism Patrol Scheduling
90 officers on LA Metro Trains
Humans required significant effort
Worse schedules than game theory
Observerrsquos report on questions
54
3
35
4
45
5
55
Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Q9
Q10
Q11
Q12
Secu
rity
Sco
re
Human Game Theory
59
Field Test Against Adversaries Mock Attackers
Example from PROTECT
ldquoMock attackerrdquo team deployed in Boston
Comparing PRE- to POST-PROTECT ldquodeterrencerdquo improved
Additional real-world indicators from Boston
Boston boaters questions
ldquohas the Coast Guard recently acquired more boatsrdquo
POST-PROTECT Actual reports of illegal activity
55
5956
Field Tests Against Adversaries
Computational Game Theory in the Field
Game theory vs Random
21 days of patrol
Identical conditions
Random + Human
Not controlled
0
20
40
60
80
100 Miscellaneous
Drugs
Firearm Violations
0
5
10
15
20
Captures30 min
Warnings30 min
Violations30 min
Game Theory
Rand+Human
Controlled
59
Expert Evaluation
Example from ARMOR IRIS amp PROTECT
February 2009 Commendations
LAX Police (City of Los Angeles)
July 2011 Operational Excellence
Award (US Coast Guard Boston)
September 2011 Certificate of
Appreciation (Federal Air Marshals)
June 2013 Meritorious Team Commendation
from Commandant (US Coast Guard)
57
59
Summary Security Games
Decision aids based on computational game theory in daily use
Optimize limited security resources against adversaries
Applications yield research challenges Science of security games
Scale-up Incremental strategy generation amp Marginals
Uncertainty Integrate MDPs Robustness Quantal response
Current applications (wildlife security) Interdisciplinar challenge
Global challenges Merge planninglearning amp security games
58
59
Just the Beginning of ldquoSecurity Gamesrdquohellip
Our next steps
59
Thank you
to sponsors
StartupARMORWAY Game theory in the field
Follow on research amp applications
Privacy audits
(Sinha IJCAIrsquo13)
Software testing
(Kukreja ASErsquo13)
Sport event security
(Yin AAAIrsquo14)
Singapore train
(Varakantham IAAIrsquo13)
Exam questions
(Li IJCAIrsquo13)
59
THANK YOU
tambeuscedu
httpteamcoreuscedusecurity
60
59
TRUSTS Frequent adversary interaction games
Patrols Against Fare Evaders
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
017
013
010
010
37
59
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
38
Jiang Delle Fave
59
Markov Decision Problems in Security games
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
39
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution Jiang Delle Fave
5940
Urban Transportation Security
MDPs amp DEC-MDPs in Security Games
COPS LA Metro System(Against Opportunistic Crime)
STREETS Singapore Roads(Against Reckless Driving)
DEC-MDPs in Security Games Teamwork + Uncertainty
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
ZhangShieh
59
Uncertainty Space Algorithms
Bayesian and Robust Approaches
Adversary payoff uncertainty
Adversary observation amp
defender execution
uncertainty
Adversary rationality
uncertainty
RECON
BRASS
Monotonic Maximin
(Monotonic adversary)
41
URAC
Payoff interval
Not point
estimateGMC HUNTER
Bayesian
Robust
Yin Nguyen An
0
05
1
15
5 10
Def
end
ers
EU
Targets
ISG RECON URAC
59
Security Games Environmental Crime
amp Bounded Rationality
FisheryGulf of Mexico
Nakai Nam Theun
Forest Area Laos
WildlifeQueen Elizabeth National
ParkUganda
No patrols
Higher
density
Lower densityx
Yang Ford Brown
59
Uncertainty in Adversary Decision Bounded Rationality
Human Subjects as Poachers
59
Uncertainty in Adversary Decision[2009]
Human subjects Anchoring e-Optimality
-4
-3
-2
-1
0
1
Unobserved 5 Observations 20 Observations Unlimited
Average expected reward
DOBSS
Uniform
COBRA
ARMOR
ARMOR Outperforms uniform random
COBRA
MqxCaq
Xxxts
qxR
ji
Xi
ijj
Xi Qj
jiijqx
)1()()1(
|)|1()1(
max
ee
Anchoring
e-optimality
With Sarit Kraus 44
Pita
5945
Quantal Response Stochastic choice better choice more likely
Quantal Response Model of Adversary [2011] Not Maximize Expected Utility
T
j
jxadversary
EU
jxadversary
EU
j
e
eq
1
))((
))((
-3
-25
-2
-15
-1
-05
0
05
1
15
2
Payoff 1 Payoff 2 Payoff 3 Payoff 4
QuantalResponse
COBRA
ARMOR
Adversaryrsquos probability
of choosing target j
Yang
10
)(max1
1
)(
)(
t tt
defendT
jT
j
jxEU
jxEU
x
xKxts
jxEU
e
e
adversary
adversary
59
Uncertainty in Adversary Decision [2012]
Robust vs Modeling Adversaries
Robustness Bound loss to defender Not model attacker via QR
Defeating Robust Learned subjective utility
Robust wins Draw QR wins
42 52 6
Results on 100 games
SU-QR wins Draw Robust
13 8 1
Results on 22 games
MqxCa
qxR
ji
Xi
ij
jiij
Xi Qj
qx
)1()(0
max
β (Adversaryrsquos utility loss if
deviates from optimal) gt=
(Defenderrsquos utility loss due to
adversary deviation)
penaltyattackw
rewardattackw
probcapturewjaSEU
3
2
1)(
M
j
jxSEU
jxSEU
jadversary
adversary
e
eq
1
)(
)(
SU-QR wins Draw Robust
6 13 3
Results against security experts
With Sarit Kraus 46
5947
PAWS Protection Assistant for Wildlife Security[2014]Repeated Stackelberg Game
Simulation
Learn from crime data
Improve model
Defender calculates
mixed strategy
Defender executes
randomized patrols
Poachers attack targets
119890119878119864119880119894(w1w2w3)
119896 119890119878119864119880119896(w1w2w3)
Bayesian SUQR Heterogeneous Poachers
5948
PAWS Test April 2014
Trials in Queen Elizabeth National Park
Andrew Lemieux with rangers
on PAWS patrol in Uganda
59
Security Resource OptimizationEvaluating Deployed Security Systems Not Easy
Game theory Improvement over previous approaches
Previous Human schedulers or ldquosimple randomrdquo
49
Lab
Evaluation
Simulated
adversary
Human subject
adversaries
Field Evaluation
Patrol quality
Unpredictable Cover
Compare real schedules
Scheduling competition
Expert evaluation
Field Evaluation
Tests against adversaries
ldquoMock attackersrdquo
Capture rates of
real adversaries
59
Why Does Game Theory Perform Better
Weaknesses of Previous Methods
Human schedulers
Predictable patterns eg US Coast Guard
Scheduling effort amp cognitive burden
Simple random (eg dice roll)
Wrong weightscoverage eg officers to sparsely crowded terminals
No adversary reactions
Multiple deployments over multiple years without us forcing them
50
59
Lab Evaluation via Simulations
Example from IRIS (FAMS)
-10
-8
-6
-4
-2
0
2
4
6
50 150 250
Defe
nde
r E
xpe
cte
d u
tilit
y
Schedule Size
Uniform Weighted random 1 Weighted random 2 IRIS
51
59
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage
Patrols Before PROTECT Boston Patrols After PROTECT Boston
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Base Patrol Area
52
PROTECT (Coast Guard) 350 increase defender expected utility
59
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage for Less Effort
53
IRIS for FAMS Outperformed expert human over six monthsReportGAO-09-903T
ARMOR at LAX Savings of up to an hour a day in scheduling
59
Field Evaluation Human vs Game Theory Competition
Counter-terrorism Patrol Scheduling
90 officers on LA Metro Trains
Humans required significant effort
Worse schedules than game theory
Observerrsquos report on questions
54
3
35
4
45
5
55
Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Q9
Q10
Q11
Q12
Secu
rity
Sco
re
Human Game Theory
59
Field Test Against Adversaries Mock Attackers
Example from PROTECT
ldquoMock attackerrdquo team deployed in Boston
Comparing PRE- to POST-PROTECT ldquodeterrencerdquo improved
Additional real-world indicators from Boston
Boston boaters questions
ldquohas the Coast Guard recently acquired more boatsrdquo
POST-PROTECT Actual reports of illegal activity
55
5956
Field Tests Against Adversaries
Computational Game Theory in the Field
Game theory vs Random
21 days of patrol
Identical conditions
Random + Human
Not controlled
0
20
40
60
80
100 Miscellaneous
Drugs
Firearm Violations
0
5
10
15
20
Captures30 min
Warnings30 min
Violations30 min
Game Theory
Rand+Human
Controlled
59
Expert Evaluation
Example from ARMOR IRIS amp PROTECT
February 2009 Commendations
LAX Police (City of Los Angeles)
July 2011 Operational Excellence
Award (US Coast Guard Boston)
September 2011 Certificate of
Appreciation (Federal Air Marshals)
June 2013 Meritorious Team Commendation
from Commandant (US Coast Guard)
57
59
Summary Security Games
Decision aids based on computational game theory in daily use
Optimize limited security resources against adversaries
Applications yield research challenges Science of security games
Scale-up Incremental strategy generation amp Marginals
Uncertainty Integrate MDPs Robustness Quantal response
Current applications (wildlife security) Interdisciplinar challenge
Global challenges Merge planninglearning amp security games
58
59
Just the Beginning of ldquoSecurity Gamesrdquohellip
Our next steps
59
Thank you
to sponsors
StartupARMORWAY Game theory in the field
Follow on research amp applications
Privacy audits
(Sinha IJCAIrsquo13)
Software testing
(Kukreja ASErsquo13)
Sport event security
(Yin AAAIrsquo14)
Singapore train
(Varakantham IAAIrsquo13)
Exam questions
(Li IJCAIrsquo13)
59
THANK YOU
tambeuscedu
httpteamcoreuscedusecurity
60
59
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
38
Jiang Delle Fave
59
Markov Decision Problems in Security games
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
39
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution Jiang Delle Fave
5940
Urban Transportation Security
MDPs amp DEC-MDPs in Security Games
COPS LA Metro System(Against Opportunistic Crime)
STREETS Singapore Roads(Against Reckless Driving)
DEC-MDPs in Security Games Teamwork + Uncertainty
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
ZhangShieh
59
Uncertainty Space Algorithms
Bayesian and Robust Approaches
Adversary payoff uncertainty
Adversary observation amp
defender execution
uncertainty
Adversary rationality
uncertainty
RECON
BRASS
Monotonic Maximin
(Monotonic adversary)
41
URAC
Payoff interval
Not point
estimateGMC HUNTER
Bayesian
Robust
Yin Nguyen An
0
05
1
15
5 10
Def
end
ers
EU
Targets
ISG RECON URAC
59
Security Games Environmental Crime
amp Bounded Rationality
FisheryGulf of Mexico
Nakai Nam Theun
Forest Area Laos
WildlifeQueen Elizabeth National
ParkUganda
No patrols
Higher
density
Lower densityx
Yang Ford Brown
59
Uncertainty in Adversary Decision Bounded Rationality
Human Subjects as Poachers
59
Uncertainty in Adversary Decision[2009]
Human subjects Anchoring e-Optimality
-4
-3
-2
-1
0
1
Unobserved 5 Observations 20 Observations Unlimited
Average expected reward
DOBSS
Uniform
COBRA
ARMOR
ARMOR Outperforms uniform random
COBRA
MqxCaq
Xxxts
qxR
ji
Xi
ijj
Xi Qj
jiijqx
)1()()1(
|)|1()1(
max
ee
Anchoring
e-optimality
With Sarit Kraus 44
Pita
5945
Quantal Response Stochastic choice better choice more likely
Quantal Response Model of Adversary [2011] Not Maximize Expected Utility
T
j
jxadversary
EU
jxadversary
EU
j
e
eq
1
))((
))((
-3
-25
-2
-15
-1
-05
0
05
1
15
2
Payoff 1 Payoff 2 Payoff 3 Payoff 4
QuantalResponse
COBRA
ARMOR
Adversaryrsquos probability
of choosing target j
Yang
10
)(max1
1
)(
)(
t tt
defendT
jT
j
jxEU
jxEU
x
xKxts
jxEU
e
e
adversary
adversary
59
Uncertainty in Adversary Decision [2012]
Robust vs Modeling Adversaries
Robustness Bound loss to defender Not model attacker via QR
Defeating Robust Learned subjective utility
Robust wins Draw QR wins
42 52 6
Results on 100 games
SU-QR wins Draw Robust
13 8 1
Results on 22 games
MqxCa
qxR
ji
Xi
ij
jiij
Xi Qj
qx
)1()(0
max
β (Adversaryrsquos utility loss if
deviates from optimal) gt=
(Defenderrsquos utility loss due to
adversary deviation)
penaltyattackw
rewardattackw
probcapturewjaSEU
3
2
1)(
M
j
jxSEU
jxSEU
jadversary
adversary
e
eq
1
)(
)(
SU-QR wins Draw Robust
6 13 3
Results against security experts
With Sarit Kraus 46
5947
PAWS Protection Assistant for Wildlife Security[2014]Repeated Stackelberg Game
Simulation
Learn from crime data
Improve model
Defender calculates
mixed strategy
Defender executes
randomized patrols
Poachers attack targets
119890119878119864119880119894(w1w2w3)
119896 119890119878119864119880119896(w1w2w3)
Bayesian SUQR Heterogeneous Poachers
5948
PAWS Test April 2014
Trials in Queen Elizabeth National Park
Andrew Lemieux with rangers
on PAWS patrol in Uganda
59
Security Resource OptimizationEvaluating Deployed Security Systems Not Easy
Game theory Improvement over previous approaches
Previous Human schedulers or ldquosimple randomrdquo
49
Lab
Evaluation
Simulated
adversary
Human subject
adversaries
Field Evaluation
Patrol quality
Unpredictable Cover
Compare real schedules
Scheduling competition
Expert evaluation
Field Evaluation
Tests against adversaries
ldquoMock attackersrdquo
Capture rates of
real adversaries
59
Why Does Game Theory Perform Better
Weaknesses of Previous Methods
Human schedulers
Predictable patterns eg US Coast Guard
Scheduling effort amp cognitive burden
Simple random (eg dice roll)
Wrong weightscoverage eg officers to sparsely crowded terminals
No adversary reactions
Multiple deployments over multiple years without us forcing them
50
59
Lab Evaluation via Simulations
Example from IRIS (FAMS)
-10
-8
-6
-4
-2
0
2
4
6
50 150 250
Defe
nde
r E
xpe
cte
d u
tilit
y
Schedule Size
Uniform Weighted random 1 Weighted random 2 IRIS
51
59
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage
Patrols Before PROTECT Boston Patrols After PROTECT Boston
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Base Patrol Area
52
PROTECT (Coast Guard) 350 increase defender expected utility
59
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage for Less Effort
53
IRIS for FAMS Outperformed expert human over six monthsReportGAO-09-903T
ARMOR at LAX Savings of up to an hour a day in scheduling
59
Field Evaluation Human vs Game Theory Competition
Counter-terrorism Patrol Scheduling
90 officers on LA Metro Trains
Humans required significant effort
Worse schedules than game theory
Observerrsquos report on questions
54
3
35
4
45
5
55
Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Q9
Q10
Q11
Q12
Secu
rity
Sco
re
Human Game Theory
59
Field Test Against Adversaries Mock Attackers
Example from PROTECT
ldquoMock attackerrdquo team deployed in Boston
Comparing PRE- to POST-PROTECT ldquodeterrencerdquo improved
Additional real-world indicators from Boston
Boston boaters questions
ldquohas the Coast Guard recently acquired more boatsrdquo
POST-PROTECT Actual reports of illegal activity
55
5956
Field Tests Against Adversaries
Computational Game Theory in the Field
Game theory vs Random
21 days of patrol
Identical conditions
Random + Human
Not controlled
0
20
40
60
80
100 Miscellaneous
Drugs
Firearm Violations
0
5
10
15
20
Captures30 min
Warnings30 min
Violations30 min
Game Theory
Rand+Human
Controlled
59
Expert Evaluation
Example from ARMOR IRIS amp PROTECT
February 2009 Commendations
LAX Police (City of Los Angeles)
July 2011 Operational Excellence
Award (US Coast Guard Boston)
September 2011 Certificate of
Appreciation (Federal Air Marshals)
June 2013 Meritorious Team Commendation
from Commandant (US Coast Guard)
57
59
Summary Security Games
Decision aids based on computational game theory in daily use
Optimize limited security resources against adversaries
Applications yield research challenges Science of security games
Scale-up Incremental strategy generation amp Marginals
Uncertainty Integrate MDPs Robustness Quantal response
Current applications (wildlife security) Interdisciplinar challenge
Global challenges Merge planninglearning amp security games
58
59
Just the Beginning of ldquoSecurity Gamesrdquohellip
Our next steps
59
Thank you
to sponsors
StartupARMORWAY Game theory in the field
Follow on research amp applications
Privacy audits
(Sinha IJCAIrsquo13)
Software testing
(Kukreja ASErsquo13)
Sport event security
(Yin AAAIrsquo14)
Singapore train
(Varakantham IAAIrsquo13)
Exam questions
(Li IJCAIrsquo13)
59
THANK YOU
tambeuscedu
httpteamcoreuscedusecurity
60
59
Markov Decision Problems in Security games
A 5 min A 10 min A 15 min
B 5 min B 10 min B 15 min
C 5 min C 10 min C 15 min
A
B
C
5 min 10 min 15 min
030
015
010
010
007
003
005
39
TRUSTS Frequent adversary interaction
Uncertainty in Defender Action Execution Jiang Delle Fave
5940
Urban Transportation Security
MDPs amp DEC-MDPs in Security Games
COPS LA Metro System(Against Opportunistic Crime)
STREETS Singapore Roads(Against Reckless Driving)
DEC-MDPs in Security Games Teamwork + Uncertainty
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
ZhangShieh
59
Uncertainty Space Algorithms
Bayesian and Robust Approaches
Adversary payoff uncertainty
Adversary observation amp
defender execution
uncertainty
Adversary rationality
uncertainty
RECON
BRASS
Monotonic Maximin
(Monotonic adversary)
41
URAC
Payoff interval
Not point
estimateGMC HUNTER
Bayesian
Robust
Yin Nguyen An
0
05
1
15
5 10
Def
end
ers
EU
Targets
ISG RECON URAC
59
Security Games Environmental Crime
amp Bounded Rationality
FisheryGulf of Mexico
Nakai Nam Theun
Forest Area Laos
WildlifeQueen Elizabeth National
ParkUganda
No patrols
Higher
density
Lower densityx
Yang Ford Brown
59
Uncertainty in Adversary Decision Bounded Rationality
Human Subjects as Poachers
59
Uncertainty in Adversary Decision[2009]
Human subjects Anchoring e-Optimality
-4
-3
-2
-1
0
1
Unobserved 5 Observations 20 Observations Unlimited
Average expected reward
DOBSS
Uniform
COBRA
ARMOR
ARMOR Outperforms uniform random
COBRA
MqxCaq
Xxxts
qxR
ji
Xi
ijj
Xi Qj
jiijqx
)1()()1(
|)|1()1(
max
ee
Anchoring
e-optimality
With Sarit Kraus 44
Pita
5945
Quantal Response Stochastic choice better choice more likely
Quantal Response Model of Adversary [2011] Not Maximize Expected Utility
T
j
jxadversary
EU
jxadversary
EU
j
e
eq
1
))((
))((
-3
-25
-2
-15
-1
-05
0
05
1
15
2
Payoff 1 Payoff 2 Payoff 3 Payoff 4
QuantalResponse
COBRA
ARMOR
Adversaryrsquos probability
of choosing target j
Yang
10
)(max1
1
)(
)(
t tt
defendT
jT
j
jxEU
jxEU
x
xKxts
jxEU
e
e
adversary
adversary
59
Uncertainty in Adversary Decision [2012]
Robust vs Modeling Adversaries
Robustness Bound loss to defender Not model attacker via QR
Defeating Robust Learned subjective utility
Robust wins Draw QR wins
42 52 6
Results on 100 games
SU-QR wins Draw Robust
13 8 1
Results on 22 games
MqxCa
qxR
ji
Xi
ij
jiij
Xi Qj
qx
)1()(0
max
β (Adversaryrsquos utility loss if
deviates from optimal) gt=
(Defenderrsquos utility loss due to
adversary deviation)
penaltyattackw
rewardattackw
probcapturewjaSEU
3
2
1)(
M
j
jxSEU
jxSEU
jadversary
adversary
e
eq
1
)(
)(
SU-QR wins Draw Robust
6 13 3
Results against security experts
With Sarit Kraus 46
5947
PAWS Protection Assistant for Wildlife Security[2014]Repeated Stackelberg Game
Simulation
Learn from crime data
Improve model
Defender calculates
mixed strategy
Defender executes
randomized patrols
Poachers attack targets
119890119878119864119880119894(w1w2w3)
119896 119890119878119864119880119896(w1w2w3)
Bayesian SUQR Heterogeneous Poachers
5948
PAWS Test April 2014
Trials in Queen Elizabeth National Park
Andrew Lemieux with rangers
on PAWS patrol in Uganda
59
Security Resource OptimizationEvaluating Deployed Security Systems Not Easy
Game theory Improvement over previous approaches
Previous Human schedulers or ldquosimple randomrdquo
49
Lab
Evaluation
Simulated
adversary
Human subject
adversaries
Field Evaluation
Patrol quality
Unpredictable Cover
Compare real schedules
Scheduling competition
Expert evaluation
Field Evaluation
Tests against adversaries
ldquoMock attackersrdquo
Capture rates of
real adversaries
59
Why Does Game Theory Perform Better
Weaknesses of Previous Methods
Human schedulers
Predictable patterns eg US Coast Guard
Scheduling effort amp cognitive burden
Simple random (eg dice roll)
Wrong weightscoverage eg officers to sparsely crowded terminals
No adversary reactions
Multiple deployments over multiple years without us forcing them
50
59
Lab Evaluation via Simulations
Example from IRIS (FAMS)
-10
-8
-6
-4
-2
0
2
4
6
50 150 250
Defe
nde
r E
xpe
cte
d u
tilit
y
Schedule Size
Uniform Weighted random 1 Weighted random 2 IRIS
51
59
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage
Patrols Before PROTECT Boston Patrols After PROTECT Boston
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Base Patrol Area
52
PROTECT (Coast Guard) 350 increase defender expected utility
59
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage for Less Effort
53
IRIS for FAMS Outperformed expert human over six monthsReportGAO-09-903T
ARMOR at LAX Savings of up to an hour a day in scheduling
59
Field Evaluation Human vs Game Theory Competition
Counter-terrorism Patrol Scheduling
90 officers on LA Metro Trains
Humans required significant effort
Worse schedules than game theory
Observerrsquos report on questions
54
3
35
4
45
5
55
Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Q9
Q10
Q11
Q12
Secu
rity
Sco
re
Human Game Theory
59
Field Test Against Adversaries Mock Attackers
Example from PROTECT
ldquoMock attackerrdquo team deployed in Boston
Comparing PRE- to POST-PROTECT ldquodeterrencerdquo improved
Additional real-world indicators from Boston
Boston boaters questions
ldquohas the Coast Guard recently acquired more boatsrdquo
POST-PROTECT Actual reports of illegal activity
55
5956
Field Tests Against Adversaries
Computational Game Theory in the Field
Game theory vs Random
21 days of patrol
Identical conditions
Random + Human
Not controlled
0
20
40
60
80
100 Miscellaneous
Drugs
Firearm Violations
0
5
10
15
20
Captures30 min
Warnings30 min
Violations30 min
Game Theory
Rand+Human
Controlled
59
Expert Evaluation
Example from ARMOR IRIS amp PROTECT
February 2009 Commendations
LAX Police (City of Los Angeles)
July 2011 Operational Excellence
Award (US Coast Guard Boston)
September 2011 Certificate of
Appreciation (Federal Air Marshals)
June 2013 Meritorious Team Commendation
from Commandant (US Coast Guard)
57
59
Summary Security Games
Decision aids based on computational game theory in daily use
Optimize limited security resources against adversaries
Applications yield research challenges Science of security games
Scale-up Incremental strategy generation amp Marginals
Uncertainty Integrate MDPs Robustness Quantal response
Current applications (wildlife security) Interdisciplinar challenge
Global challenges Merge planninglearning amp security games
58
59
Just the Beginning of ldquoSecurity Gamesrdquohellip
Our next steps
59
Thank you
to sponsors
StartupARMORWAY Game theory in the field
Follow on research amp applications
Privacy audits
(Sinha IJCAIrsquo13)
Software testing
(Kukreja ASErsquo13)
Sport event security
(Yin AAAIrsquo14)
Singapore train
(Varakantham IAAIrsquo13)
Exam questions
(Li IJCAIrsquo13)
59
THANK YOU
tambeuscedu
httpteamcoreuscedusecurity
60
5940
Urban Transportation Security
MDPs amp DEC-MDPs in Security Games
COPS LA Metro System(Against Opportunistic Crime)
STREETS Singapore Roads(Against Reckless Driving)
DEC-MDPs in Security Games Teamwork + Uncertainty
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
A 5 A 10 A 15
B 5 B 10 B 15
C 5 C 10 C 15
ZhangShieh
59
Uncertainty Space Algorithms
Bayesian and Robust Approaches
Adversary payoff uncertainty
Adversary observation amp
defender execution
uncertainty
Adversary rationality
uncertainty
RECON
BRASS
Monotonic Maximin
(Monotonic adversary)
41
URAC
Payoff interval
Not point
estimateGMC HUNTER
Bayesian
Robust
Yin Nguyen An
0
05
1
15
5 10
Def
end
ers
EU
Targets
ISG RECON URAC
59
Security Games Environmental Crime
amp Bounded Rationality
FisheryGulf of Mexico
Nakai Nam Theun
Forest Area Laos
WildlifeQueen Elizabeth National
ParkUganda
No patrols
Higher
density
Lower densityx
Yang Ford Brown
59
Uncertainty in Adversary Decision Bounded Rationality
Human Subjects as Poachers
59
Uncertainty in Adversary Decision[2009]
Human subjects Anchoring e-Optimality
-4
-3
-2
-1
0
1
Unobserved 5 Observations 20 Observations Unlimited
Average expected reward
DOBSS
Uniform
COBRA
ARMOR
ARMOR Outperforms uniform random
COBRA
MqxCaq
Xxxts
qxR
ji
Xi
ijj
Xi Qj
jiijqx
)1()()1(
|)|1()1(
max
ee
Anchoring
e-optimality
With Sarit Kraus 44
Pita
5945
Quantal Response Stochastic choice better choice more likely
Quantal Response Model of Adversary [2011] Not Maximize Expected Utility
T
j
jxadversary
EU
jxadversary
EU
j
e
eq
1
))((
))((
-3
-25
-2
-15
-1
-05
0
05
1
15
2
Payoff 1 Payoff 2 Payoff 3 Payoff 4
QuantalResponse
COBRA
ARMOR
Adversaryrsquos probability
of choosing target j
Yang
10
)(max1
1
)(
)(
t tt
defendT
jT
j
jxEU
jxEU
x
xKxts
jxEU
e
e
adversary
adversary
59
Uncertainty in Adversary Decision [2012]
Robust vs Modeling Adversaries
Robustness Bound loss to defender Not model attacker via QR
Defeating Robust Learned subjective utility
Robust wins Draw QR wins
42 52 6
Results on 100 games
SU-QR wins Draw Robust
13 8 1
Results on 22 games
MqxCa
qxR
ji
Xi
ij
jiij
Xi Qj
qx
)1()(0
max
β (Adversaryrsquos utility loss if
deviates from optimal) gt=
(Defenderrsquos utility loss due to
adversary deviation)
penaltyattackw
rewardattackw
probcapturewjaSEU
3
2
1)(
M
j
jxSEU
jxSEU
jadversary
adversary
e
eq
1
)(
)(
SU-QR wins Draw Robust
6 13 3
Results against security experts
With Sarit Kraus 46
5947
PAWS Protection Assistant for Wildlife Security[2014]Repeated Stackelberg Game
Simulation
Learn from crime data
Improve model
Defender calculates
mixed strategy
Defender executes
randomized patrols
Poachers attack targets
119890119878119864119880119894(w1w2w3)
119896 119890119878119864119880119896(w1w2w3)
Bayesian SUQR Heterogeneous Poachers
5948
PAWS Test April 2014
Trials in Queen Elizabeth National Park
Andrew Lemieux with rangers
on PAWS patrol in Uganda
59
Security Resource OptimizationEvaluating Deployed Security Systems Not Easy
Game theory Improvement over previous approaches
Previous Human schedulers or ldquosimple randomrdquo
49
Lab
Evaluation
Simulated
adversary
Human subject
adversaries
Field Evaluation
Patrol quality
Unpredictable Cover
Compare real schedules
Scheduling competition
Expert evaluation
Field Evaluation
Tests against adversaries
ldquoMock attackersrdquo
Capture rates of
real adversaries
59
Why Does Game Theory Perform Better
Weaknesses of Previous Methods
Human schedulers
Predictable patterns eg US Coast Guard
Scheduling effort amp cognitive burden
Simple random (eg dice roll)
Wrong weightscoverage eg officers to sparsely crowded terminals
No adversary reactions
Multiple deployments over multiple years without us forcing them
50
59
Lab Evaluation via Simulations
Example from IRIS (FAMS)
-10
-8
-6
-4
-2
0
2
4
6
50 150 250
Defe
nde
r E
xpe
cte
d u
tilit
y
Schedule Size
Uniform Weighted random 1 Weighted random 2 IRIS
51
59
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage
Patrols Before PROTECT Boston Patrols After PROTECT Boston
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Base Patrol Area
52
PROTECT (Coast Guard) 350 increase defender expected utility
59
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage for Less Effort
53
IRIS for FAMS Outperformed expert human over six monthsReportGAO-09-903T
ARMOR at LAX Savings of up to an hour a day in scheduling
59
Field Evaluation Human vs Game Theory Competition
Counter-terrorism Patrol Scheduling
90 officers on LA Metro Trains
Humans required significant effort
Worse schedules than game theory
Observerrsquos report on questions
54
3
35
4
45
5
55
Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Q9
Q10
Q11
Q12
Secu
rity
Sco
re
Human Game Theory
59
Field Test Against Adversaries Mock Attackers
Example from PROTECT
ldquoMock attackerrdquo team deployed in Boston
Comparing PRE- to POST-PROTECT ldquodeterrencerdquo improved
Additional real-world indicators from Boston
Boston boaters questions
ldquohas the Coast Guard recently acquired more boatsrdquo
POST-PROTECT Actual reports of illegal activity
55
5956
Field Tests Against Adversaries
Computational Game Theory in the Field
Game theory vs Random
21 days of patrol
Identical conditions
Random + Human
Not controlled
0
20
40
60
80
100 Miscellaneous
Drugs
Firearm Violations
0
5
10
15
20
Captures30 min
Warnings30 min
Violations30 min
Game Theory
Rand+Human
Controlled
59
Expert Evaluation
Example from ARMOR IRIS amp PROTECT
February 2009 Commendations
LAX Police (City of Los Angeles)
July 2011 Operational Excellence
Award (US Coast Guard Boston)
September 2011 Certificate of
Appreciation (Federal Air Marshals)
June 2013 Meritorious Team Commendation
from Commandant (US Coast Guard)
57
59
Summary Security Games
Decision aids based on computational game theory in daily use
Optimize limited security resources against adversaries
Applications yield research challenges Science of security games
Scale-up Incremental strategy generation amp Marginals
Uncertainty Integrate MDPs Robustness Quantal response
Current applications (wildlife security) Interdisciplinar challenge
Global challenges Merge planninglearning amp security games
58
59
Just the Beginning of ldquoSecurity Gamesrdquohellip
Our next steps
59
Thank you
to sponsors
StartupARMORWAY Game theory in the field
Follow on research amp applications
Privacy audits
(Sinha IJCAIrsquo13)
Software testing
(Kukreja ASErsquo13)
Sport event security
(Yin AAAIrsquo14)
Singapore train
(Varakantham IAAIrsquo13)
Exam questions
(Li IJCAIrsquo13)
59
THANK YOU
tambeuscedu
httpteamcoreuscedusecurity
60
59
Uncertainty Space Algorithms
Bayesian and Robust Approaches
Adversary payoff uncertainty
Adversary observation amp
defender execution
uncertainty
Adversary rationality
uncertainty
RECON
BRASS
Monotonic Maximin
(Monotonic adversary)
41
URAC
Payoff interval
Not point
estimateGMC HUNTER
Bayesian
Robust
Yin Nguyen An
0
05
1
15
5 10
Def
end
ers
EU
Targets
ISG RECON URAC
59
Security Games Environmental Crime
amp Bounded Rationality
FisheryGulf of Mexico
Nakai Nam Theun
Forest Area Laos
WildlifeQueen Elizabeth National
ParkUganda
No patrols
Higher
density
Lower densityx
Yang Ford Brown
59
Uncertainty in Adversary Decision Bounded Rationality
Human Subjects as Poachers
59
Uncertainty in Adversary Decision[2009]
Human subjects Anchoring e-Optimality
-4
-3
-2
-1
0
1
Unobserved 5 Observations 20 Observations Unlimited
Average expected reward
DOBSS
Uniform
COBRA
ARMOR
ARMOR Outperforms uniform random
COBRA
MqxCaq
Xxxts
qxR
ji
Xi
ijj
Xi Qj
jiijqx
)1()()1(
|)|1()1(
max
ee
Anchoring
e-optimality
With Sarit Kraus 44
Pita
5945
Quantal Response Stochastic choice better choice more likely
Quantal Response Model of Adversary [2011] Not Maximize Expected Utility
T
j
jxadversary
EU
jxadversary
EU
j
e
eq
1
))((
))((
-3
-25
-2
-15
-1
-05
0
05
1
15
2
Payoff 1 Payoff 2 Payoff 3 Payoff 4
QuantalResponse
COBRA
ARMOR
Adversaryrsquos probability
of choosing target j
Yang
10
)(max1
1
)(
)(
t tt
defendT
jT
j
jxEU
jxEU
x
xKxts
jxEU
e
e
adversary
adversary
59
Uncertainty in Adversary Decision [2012]
Robust vs Modeling Adversaries
Robustness Bound loss to defender Not model attacker via QR
Defeating Robust Learned subjective utility
Robust wins Draw QR wins
42 52 6
Results on 100 games
SU-QR wins Draw Robust
13 8 1
Results on 22 games
MqxCa
qxR
ji
Xi
ij
jiij
Xi Qj
qx
)1()(0
max
β (Adversaryrsquos utility loss if
deviates from optimal) gt=
(Defenderrsquos utility loss due to
adversary deviation)
penaltyattackw
rewardattackw
probcapturewjaSEU
3
2
1)(
M
j
jxSEU
jxSEU
jadversary
adversary
e
eq
1
)(
)(
SU-QR wins Draw Robust
6 13 3
Results against security experts
With Sarit Kraus 46
5947
PAWS Protection Assistant for Wildlife Security[2014]Repeated Stackelberg Game
Simulation
Learn from crime data
Improve model
Defender calculates
mixed strategy
Defender executes
randomized patrols
Poachers attack targets
119890119878119864119880119894(w1w2w3)
119896 119890119878119864119880119896(w1w2w3)
Bayesian SUQR Heterogeneous Poachers
5948
PAWS Test April 2014
Trials in Queen Elizabeth National Park
Andrew Lemieux with rangers
on PAWS patrol in Uganda
59
Security Resource OptimizationEvaluating Deployed Security Systems Not Easy
Game theory Improvement over previous approaches
Previous Human schedulers or ldquosimple randomrdquo
49
Lab
Evaluation
Simulated
adversary
Human subject
adversaries
Field Evaluation
Patrol quality
Unpredictable Cover
Compare real schedules
Scheduling competition
Expert evaluation
Field Evaluation
Tests against adversaries
ldquoMock attackersrdquo
Capture rates of
real adversaries
59
Why Does Game Theory Perform Better
Weaknesses of Previous Methods
Human schedulers
Predictable patterns eg US Coast Guard
Scheduling effort amp cognitive burden
Simple random (eg dice roll)
Wrong weightscoverage eg officers to sparsely crowded terminals
No adversary reactions
Multiple deployments over multiple years without us forcing them
50
59
Lab Evaluation via Simulations
Example from IRIS (FAMS)
-10
-8
-6
-4
-2
0
2
4
6
50 150 250
Defe
nde
r E
xpe
cte
d u
tilit
y
Schedule Size
Uniform Weighted random 1 Weighted random 2 IRIS
51
59
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage
Patrols Before PROTECT Boston Patrols After PROTECT Boston
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Base Patrol Area
52
PROTECT (Coast Guard) 350 increase defender expected utility
59
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage for Less Effort
53
IRIS for FAMS Outperformed expert human over six monthsReportGAO-09-903T
ARMOR at LAX Savings of up to an hour a day in scheduling
59
Field Evaluation Human vs Game Theory Competition
Counter-terrorism Patrol Scheduling
90 officers on LA Metro Trains
Humans required significant effort
Worse schedules than game theory
Observerrsquos report on questions
54
3
35
4
45
5
55
Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Q9
Q10
Q11
Q12
Secu
rity
Sco
re
Human Game Theory
59
Field Test Against Adversaries Mock Attackers
Example from PROTECT
ldquoMock attackerrdquo team deployed in Boston
Comparing PRE- to POST-PROTECT ldquodeterrencerdquo improved
Additional real-world indicators from Boston
Boston boaters questions
ldquohas the Coast Guard recently acquired more boatsrdquo
POST-PROTECT Actual reports of illegal activity
55
5956
Field Tests Against Adversaries
Computational Game Theory in the Field
Game theory vs Random
21 days of patrol
Identical conditions
Random + Human
Not controlled
0
20
40
60
80
100 Miscellaneous
Drugs
Firearm Violations
0
5
10
15
20
Captures30 min
Warnings30 min
Violations30 min
Game Theory
Rand+Human
Controlled
59
Expert Evaluation
Example from ARMOR IRIS amp PROTECT
February 2009 Commendations
LAX Police (City of Los Angeles)
July 2011 Operational Excellence
Award (US Coast Guard Boston)
September 2011 Certificate of
Appreciation (Federal Air Marshals)
June 2013 Meritorious Team Commendation
from Commandant (US Coast Guard)
57
59
Summary Security Games
Decision aids based on computational game theory in daily use
Optimize limited security resources against adversaries
Applications yield research challenges Science of security games
Scale-up Incremental strategy generation amp Marginals
Uncertainty Integrate MDPs Robustness Quantal response
Current applications (wildlife security) Interdisciplinar challenge
Global challenges Merge planninglearning amp security games
58
59
Just the Beginning of ldquoSecurity Gamesrdquohellip
Our next steps
59
Thank you
to sponsors
StartupARMORWAY Game theory in the field
Follow on research amp applications
Privacy audits
(Sinha IJCAIrsquo13)
Software testing
(Kukreja ASErsquo13)
Sport event security
(Yin AAAIrsquo14)
Singapore train
(Varakantham IAAIrsquo13)
Exam questions
(Li IJCAIrsquo13)
59
THANK YOU
tambeuscedu
httpteamcoreuscedusecurity
60
59
Security Games Environmental Crime
amp Bounded Rationality
FisheryGulf of Mexico
Nakai Nam Theun
Forest Area Laos
WildlifeQueen Elizabeth National
ParkUganda
No patrols
Higher
density
Lower densityx
Yang Ford Brown
59
Uncertainty in Adversary Decision Bounded Rationality
Human Subjects as Poachers
59
Uncertainty in Adversary Decision[2009]
Human subjects Anchoring e-Optimality
-4
-3
-2
-1
0
1
Unobserved 5 Observations 20 Observations Unlimited
Average expected reward
DOBSS
Uniform
COBRA
ARMOR
ARMOR Outperforms uniform random
COBRA
MqxCaq
Xxxts
qxR
ji
Xi
ijj
Xi Qj
jiijqx
)1()()1(
|)|1()1(
max
ee
Anchoring
e-optimality
With Sarit Kraus 44
Pita
5945
Quantal Response Stochastic choice better choice more likely
Quantal Response Model of Adversary [2011] Not Maximize Expected Utility
T
j
jxadversary
EU
jxadversary
EU
j
e
eq
1
))((
))((
-3
-25
-2
-15
-1
-05
0
05
1
15
2
Payoff 1 Payoff 2 Payoff 3 Payoff 4
QuantalResponse
COBRA
ARMOR
Adversaryrsquos probability
of choosing target j
Yang
10
)(max1
1
)(
)(
t tt
defendT
jT
j
jxEU
jxEU
x
xKxts
jxEU
e
e
adversary
adversary
59
Uncertainty in Adversary Decision [2012]
Robust vs Modeling Adversaries
Robustness Bound loss to defender Not model attacker via QR
Defeating Robust Learned subjective utility
Robust wins Draw QR wins
42 52 6
Results on 100 games
SU-QR wins Draw Robust
13 8 1
Results on 22 games
MqxCa
qxR
ji
Xi
ij
jiij
Xi Qj
qx
)1()(0
max
β (Adversaryrsquos utility loss if
deviates from optimal) gt=
(Defenderrsquos utility loss due to
adversary deviation)
penaltyattackw
rewardattackw
probcapturewjaSEU
3
2
1)(
M
j
jxSEU
jxSEU
jadversary
adversary
e
eq
1
)(
)(
SU-QR wins Draw Robust
6 13 3
Results against security experts
With Sarit Kraus 46
5947
PAWS Protection Assistant for Wildlife Security[2014]Repeated Stackelberg Game
Simulation
Learn from crime data
Improve model
Defender calculates
mixed strategy
Defender executes
randomized patrols
Poachers attack targets
119890119878119864119880119894(w1w2w3)
119896 119890119878119864119880119896(w1w2w3)
Bayesian SUQR Heterogeneous Poachers
5948
PAWS Test April 2014
Trials in Queen Elizabeth National Park
Andrew Lemieux with rangers
on PAWS patrol in Uganda
59
Security Resource OptimizationEvaluating Deployed Security Systems Not Easy
Game theory Improvement over previous approaches
Previous Human schedulers or ldquosimple randomrdquo
49
Lab
Evaluation
Simulated
adversary
Human subject
adversaries
Field Evaluation
Patrol quality
Unpredictable Cover
Compare real schedules
Scheduling competition
Expert evaluation
Field Evaluation
Tests against adversaries
ldquoMock attackersrdquo
Capture rates of
real adversaries
59
Why Does Game Theory Perform Better
Weaknesses of Previous Methods
Human schedulers
Predictable patterns eg US Coast Guard
Scheduling effort amp cognitive burden
Simple random (eg dice roll)
Wrong weightscoverage eg officers to sparsely crowded terminals
No adversary reactions
Multiple deployments over multiple years without us forcing them
50
59
Lab Evaluation via Simulations
Example from IRIS (FAMS)
-10
-8
-6
-4
-2
0
2
4
6
50 150 250
Defe
nde
r E
xpe
cte
d u
tilit
y
Schedule Size
Uniform Weighted random 1 Weighted random 2 IRIS
51
59
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage
Patrols Before PROTECT Boston Patrols After PROTECT Boston
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Base Patrol Area
52
PROTECT (Coast Guard) 350 increase defender expected utility
59
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage for Less Effort
53
IRIS for FAMS Outperformed expert human over six monthsReportGAO-09-903T
ARMOR at LAX Savings of up to an hour a day in scheduling
59
Field Evaluation Human vs Game Theory Competition
Counter-terrorism Patrol Scheduling
90 officers on LA Metro Trains
Humans required significant effort
Worse schedules than game theory
Observerrsquos report on questions
54
3
35
4
45
5
55
Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Q9
Q10
Q11
Q12
Secu
rity
Sco
re
Human Game Theory
59
Field Test Against Adversaries Mock Attackers
Example from PROTECT
ldquoMock attackerrdquo team deployed in Boston
Comparing PRE- to POST-PROTECT ldquodeterrencerdquo improved
Additional real-world indicators from Boston
Boston boaters questions
ldquohas the Coast Guard recently acquired more boatsrdquo
POST-PROTECT Actual reports of illegal activity
55
5956
Field Tests Against Adversaries
Computational Game Theory in the Field
Game theory vs Random
21 days of patrol
Identical conditions
Random + Human
Not controlled
0
20
40
60
80
100 Miscellaneous
Drugs
Firearm Violations
0
5
10
15
20
Captures30 min
Warnings30 min
Violations30 min
Game Theory
Rand+Human
Controlled
59
Expert Evaluation
Example from ARMOR IRIS amp PROTECT
February 2009 Commendations
LAX Police (City of Los Angeles)
July 2011 Operational Excellence
Award (US Coast Guard Boston)
September 2011 Certificate of
Appreciation (Federal Air Marshals)
June 2013 Meritorious Team Commendation
from Commandant (US Coast Guard)
57
59
Summary Security Games
Decision aids based on computational game theory in daily use
Optimize limited security resources against adversaries
Applications yield research challenges Science of security games
Scale-up Incremental strategy generation amp Marginals
Uncertainty Integrate MDPs Robustness Quantal response
Current applications (wildlife security) Interdisciplinar challenge
Global challenges Merge planninglearning amp security games
58
59
Just the Beginning of ldquoSecurity Gamesrdquohellip
Our next steps
59
Thank you
to sponsors
StartupARMORWAY Game theory in the field
Follow on research amp applications
Privacy audits
(Sinha IJCAIrsquo13)
Software testing
(Kukreja ASErsquo13)
Sport event security
(Yin AAAIrsquo14)
Singapore train
(Varakantham IAAIrsquo13)
Exam questions
(Li IJCAIrsquo13)
59
THANK YOU
tambeuscedu
httpteamcoreuscedusecurity
60
59
Uncertainty in Adversary Decision Bounded Rationality
Human Subjects as Poachers
59
Uncertainty in Adversary Decision[2009]
Human subjects Anchoring e-Optimality
-4
-3
-2
-1
0
1
Unobserved 5 Observations 20 Observations Unlimited
Average expected reward
DOBSS
Uniform
COBRA
ARMOR
ARMOR Outperforms uniform random
COBRA
MqxCaq
Xxxts
qxR
ji
Xi
ijj
Xi Qj
jiijqx
)1()()1(
|)|1()1(
max
ee
Anchoring
e-optimality
With Sarit Kraus 44
Pita
5945
Quantal Response Stochastic choice better choice more likely
Quantal Response Model of Adversary [2011] Not Maximize Expected Utility
T
j
jxadversary
EU
jxadversary
EU
j
e
eq
1
))((
))((
-3
-25
-2
-15
-1
-05
0
05
1
15
2
Payoff 1 Payoff 2 Payoff 3 Payoff 4
QuantalResponse
COBRA
ARMOR
Adversaryrsquos probability
of choosing target j
Yang
10
)(max1
1
)(
)(
t tt
defendT
jT
j
jxEU
jxEU
x
xKxts
jxEU
e
e
adversary
adversary
59
Uncertainty in Adversary Decision [2012]
Robust vs Modeling Adversaries
Robustness Bound loss to defender Not model attacker via QR
Defeating Robust Learned subjective utility
Robust wins Draw QR wins
42 52 6
Results on 100 games
SU-QR wins Draw Robust
13 8 1
Results on 22 games
MqxCa
qxR
ji
Xi
ij
jiij
Xi Qj
qx
)1()(0
max
β (Adversaryrsquos utility loss if
deviates from optimal) gt=
(Defenderrsquos utility loss due to
adversary deviation)
penaltyattackw
rewardattackw
probcapturewjaSEU
3
2
1)(
M
j
jxSEU
jxSEU
jadversary
adversary
e
eq
1
)(
)(
SU-QR wins Draw Robust
6 13 3
Results against security experts
With Sarit Kraus 46
5947
PAWS Protection Assistant for Wildlife Security[2014]Repeated Stackelberg Game
Simulation
Learn from crime data
Improve model
Defender calculates
mixed strategy
Defender executes
randomized patrols
Poachers attack targets
119890119878119864119880119894(w1w2w3)
119896 119890119878119864119880119896(w1w2w3)
Bayesian SUQR Heterogeneous Poachers
5948
PAWS Test April 2014
Trials in Queen Elizabeth National Park
Andrew Lemieux with rangers
on PAWS patrol in Uganda
59
Security Resource OptimizationEvaluating Deployed Security Systems Not Easy
Game theory Improvement over previous approaches
Previous Human schedulers or ldquosimple randomrdquo
49
Lab
Evaluation
Simulated
adversary
Human subject
adversaries
Field Evaluation
Patrol quality
Unpredictable Cover
Compare real schedules
Scheduling competition
Expert evaluation
Field Evaluation
Tests against adversaries
ldquoMock attackersrdquo
Capture rates of
real adversaries
59
Why Does Game Theory Perform Better
Weaknesses of Previous Methods
Human schedulers
Predictable patterns eg US Coast Guard
Scheduling effort amp cognitive burden
Simple random (eg dice roll)
Wrong weightscoverage eg officers to sparsely crowded terminals
No adversary reactions
Multiple deployments over multiple years without us forcing them
50
59
Lab Evaluation via Simulations
Example from IRIS (FAMS)
-10
-8
-6
-4
-2
0
2
4
6
50 150 250
Defe
nde
r E
xpe
cte
d u
tilit
y
Schedule Size
Uniform Weighted random 1 Weighted random 2 IRIS
51
59
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage
Patrols Before PROTECT Boston Patrols After PROTECT Boston
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Base Patrol Area
52
PROTECT (Coast Guard) 350 increase defender expected utility
59
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage for Less Effort
53
IRIS for FAMS Outperformed expert human over six monthsReportGAO-09-903T
ARMOR at LAX Savings of up to an hour a day in scheduling
59
Field Evaluation Human vs Game Theory Competition
Counter-terrorism Patrol Scheduling
90 officers on LA Metro Trains
Humans required significant effort
Worse schedules than game theory
Observerrsquos report on questions
54
3
35
4
45
5
55
Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Q9
Q10
Q11
Q12
Secu
rity
Sco
re
Human Game Theory
59
Field Test Against Adversaries Mock Attackers
Example from PROTECT
ldquoMock attackerrdquo team deployed in Boston
Comparing PRE- to POST-PROTECT ldquodeterrencerdquo improved
Additional real-world indicators from Boston
Boston boaters questions
ldquohas the Coast Guard recently acquired more boatsrdquo
POST-PROTECT Actual reports of illegal activity
55
5956
Field Tests Against Adversaries
Computational Game Theory in the Field
Game theory vs Random
21 days of patrol
Identical conditions
Random + Human
Not controlled
0
20
40
60
80
100 Miscellaneous
Drugs
Firearm Violations
0
5
10
15
20
Captures30 min
Warnings30 min
Violations30 min
Game Theory
Rand+Human
Controlled
59
Expert Evaluation
Example from ARMOR IRIS amp PROTECT
February 2009 Commendations
LAX Police (City of Los Angeles)
July 2011 Operational Excellence
Award (US Coast Guard Boston)
September 2011 Certificate of
Appreciation (Federal Air Marshals)
June 2013 Meritorious Team Commendation
from Commandant (US Coast Guard)
57
59
Summary Security Games
Decision aids based on computational game theory in daily use
Optimize limited security resources against adversaries
Applications yield research challenges Science of security games
Scale-up Incremental strategy generation amp Marginals
Uncertainty Integrate MDPs Robustness Quantal response
Current applications (wildlife security) Interdisciplinar challenge
Global challenges Merge planninglearning amp security games
58
59
Just the Beginning of ldquoSecurity Gamesrdquohellip
Our next steps
59
Thank you
to sponsors
StartupARMORWAY Game theory in the field
Follow on research amp applications
Privacy audits
(Sinha IJCAIrsquo13)
Software testing
(Kukreja ASErsquo13)
Sport event security
(Yin AAAIrsquo14)
Singapore train
(Varakantham IAAIrsquo13)
Exam questions
(Li IJCAIrsquo13)
59
THANK YOU
tambeuscedu
httpteamcoreuscedusecurity
60
59
Uncertainty in Adversary Decision[2009]
Human subjects Anchoring e-Optimality
-4
-3
-2
-1
0
1
Unobserved 5 Observations 20 Observations Unlimited
Average expected reward
DOBSS
Uniform
COBRA
ARMOR
ARMOR Outperforms uniform random
COBRA
MqxCaq
Xxxts
qxR
ji
Xi
ijj
Xi Qj
jiijqx
)1()()1(
|)|1()1(
max
ee
Anchoring
e-optimality
With Sarit Kraus 44
Pita
5945
Quantal Response Stochastic choice better choice more likely
Quantal Response Model of Adversary [2011] Not Maximize Expected Utility
T
j
jxadversary
EU
jxadversary
EU
j
e
eq
1
))((
))((
-3
-25
-2
-15
-1
-05
0
05
1
15
2
Payoff 1 Payoff 2 Payoff 3 Payoff 4
QuantalResponse
COBRA
ARMOR
Adversaryrsquos probability
of choosing target j
Yang
10
)(max1
1
)(
)(
t tt
defendT
jT
j
jxEU
jxEU
x
xKxts
jxEU
e
e
adversary
adversary
59
Uncertainty in Adversary Decision [2012]
Robust vs Modeling Adversaries
Robustness Bound loss to defender Not model attacker via QR
Defeating Robust Learned subjective utility
Robust wins Draw QR wins
42 52 6
Results on 100 games
SU-QR wins Draw Robust
13 8 1
Results on 22 games
MqxCa
qxR
ji
Xi
ij
jiij
Xi Qj
qx
)1()(0
max
β (Adversaryrsquos utility loss if
deviates from optimal) gt=
(Defenderrsquos utility loss due to
adversary deviation)
penaltyattackw
rewardattackw
probcapturewjaSEU
3
2
1)(
M
j
jxSEU
jxSEU
jadversary
adversary
e
eq
1
)(
)(
SU-QR wins Draw Robust
6 13 3
Results against security experts
With Sarit Kraus 46
5947
PAWS Protection Assistant for Wildlife Security[2014]Repeated Stackelberg Game
Simulation
Learn from crime data
Improve model
Defender calculates
mixed strategy
Defender executes
randomized patrols
Poachers attack targets
119890119878119864119880119894(w1w2w3)
119896 119890119878119864119880119896(w1w2w3)
Bayesian SUQR Heterogeneous Poachers
5948
PAWS Test April 2014
Trials in Queen Elizabeth National Park
Andrew Lemieux with rangers
on PAWS patrol in Uganda
59
Security Resource OptimizationEvaluating Deployed Security Systems Not Easy
Game theory Improvement over previous approaches
Previous Human schedulers or ldquosimple randomrdquo
49
Lab
Evaluation
Simulated
adversary
Human subject
adversaries
Field Evaluation
Patrol quality
Unpredictable Cover
Compare real schedules
Scheduling competition
Expert evaluation
Field Evaluation
Tests against adversaries
ldquoMock attackersrdquo
Capture rates of
real adversaries
59
Why Does Game Theory Perform Better
Weaknesses of Previous Methods
Human schedulers
Predictable patterns eg US Coast Guard
Scheduling effort amp cognitive burden
Simple random (eg dice roll)
Wrong weightscoverage eg officers to sparsely crowded terminals
No adversary reactions
Multiple deployments over multiple years without us forcing them
50
59
Lab Evaluation via Simulations
Example from IRIS (FAMS)
-10
-8
-6
-4
-2
0
2
4
6
50 150 250
Defe
nde
r E
xpe
cte
d u
tilit
y
Schedule Size
Uniform Weighted random 1 Weighted random 2 IRIS
51
59
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage
Patrols Before PROTECT Boston Patrols After PROTECT Boston
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Base Patrol Area
52
PROTECT (Coast Guard) 350 increase defender expected utility
59
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage for Less Effort
53
IRIS for FAMS Outperformed expert human over six monthsReportGAO-09-903T
ARMOR at LAX Savings of up to an hour a day in scheduling
59
Field Evaluation Human vs Game Theory Competition
Counter-terrorism Patrol Scheduling
90 officers on LA Metro Trains
Humans required significant effort
Worse schedules than game theory
Observerrsquos report on questions
54
3
35
4
45
5
55
Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Q9
Q10
Q11
Q12
Secu
rity
Sco
re
Human Game Theory
59
Field Test Against Adversaries Mock Attackers
Example from PROTECT
ldquoMock attackerrdquo team deployed in Boston
Comparing PRE- to POST-PROTECT ldquodeterrencerdquo improved
Additional real-world indicators from Boston
Boston boaters questions
ldquohas the Coast Guard recently acquired more boatsrdquo
POST-PROTECT Actual reports of illegal activity
55
5956
Field Tests Against Adversaries
Computational Game Theory in the Field
Game theory vs Random
21 days of patrol
Identical conditions
Random + Human
Not controlled
0
20
40
60
80
100 Miscellaneous
Drugs
Firearm Violations
0
5
10
15
20
Captures30 min
Warnings30 min
Violations30 min
Game Theory
Rand+Human
Controlled
59
Expert Evaluation
Example from ARMOR IRIS amp PROTECT
February 2009 Commendations
LAX Police (City of Los Angeles)
July 2011 Operational Excellence
Award (US Coast Guard Boston)
September 2011 Certificate of
Appreciation (Federal Air Marshals)
June 2013 Meritorious Team Commendation
from Commandant (US Coast Guard)
57
59
Summary Security Games
Decision aids based on computational game theory in daily use
Optimize limited security resources against adversaries
Applications yield research challenges Science of security games
Scale-up Incremental strategy generation amp Marginals
Uncertainty Integrate MDPs Robustness Quantal response
Current applications (wildlife security) Interdisciplinar challenge
Global challenges Merge planninglearning amp security games
58
59
Just the Beginning of ldquoSecurity Gamesrdquohellip
Our next steps
59
Thank you
to sponsors
StartupARMORWAY Game theory in the field
Follow on research amp applications
Privacy audits
(Sinha IJCAIrsquo13)
Software testing
(Kukreja ASErsquo13)
Sport event security
(Yin AAAIrsquo14)
Singapore train
(Varakantham IAAIrsquo13)
Exam questions
(Li IJCAIrsquo13)
59
THANK YOU
tambeuscedu
httpteamcoreuscedusecurity
60
5945
Quantal Response Stochastic choice better choice more likely
Quantal Response Model of Adversary [2011] Not Maximize Expected Utility
T
j
jxadversary
EU
jxadversary
EU
j
e
eq
1
))((
))((
-3
-25
-2
-15
-1
-05
0
05
1
15
2
Payoff 1 Payoff 2 Payoff 3 Payoff 4
QuantalResponse
COBRA
ARMOR
Adversaryrsquos probability
of choosing target j
Yang
10
)(max1
1
)(
)(
t tt
defendT
jT
j
jxEU
jxEU
x
xKxts
jxEU
e
e
adversary
adversary
59
Uncertainty in Adversary Decision [2012]
Robust vs Modeling Adversaries
Robustness Bound loss to defender Not model attacker via QR
Defeating Robust Learned subjective utility
Robust wins Draw QR wins
42 52 6
Results on 100 games
SU-QR wins Draw Robust
13 8 1
Results on 22 games
MqxCa
qxR
ji
Xi
ij
jiij
Xi Qj
qx
)1()(0
max
β (Adversaryrsquos utility loss if
deviates from optimal) gt=
(Defenderrsquos utility loss due to
adversary deviation)
penaltyattackw
rewardattackw
probcapturewjaSEU
3
2
1)(
M
j
jxSEU
jxSEU
jadversary
adversary
e
eq
1
)(
)(
SU-QR wins Draw Robust
6 13 3
Results against security experts
With Sarit Kraus 46
5947
PAWS Protection Assistant for Wildlife Security[2014]Repeated Stackelberg Game
Simulation
Learn from crime data
Improve model
Defender calculates
mixed strategy
Defender executes
randomized patrols
Poachers attack targets
119890119878119864119880119894(w1w2w3)
119896 119890119878119864119880119896(w1w2w3)
Bayesian SUQR Heterogeneous Poachers
5948
PAWS Test April 2014
Trials in Queen Elizabeth National Park
Andrew Lemieux with rangers
on PAWS patrol in Uganda
59
Security Resource OptimizationEvaluating Deployed Security Systems Not Easy
Game theory Improvement over previous approaches
Previous Human schedulers or ldquosimple randomrdquo
49
Lab
Evaluation
Simulated
adversary
Human subject
adversaries
Field Evaluation
Patrol quality
Unpredictable Cover
Compare real schedules
Scheduling competition
Expert evaluation
Field Evaluation
Tests against adversaries
ldquoMock attackersrdquo
Capture rates of
real adversaries
59
Why Does Game Theory Perform Better
Weaknesses of Previous Methods
Human schedulers
Predictable patterns eg US Coast Guard
Scheduling effort amp cognitive burden
Simple random (eg dice roll)
Wrong weightscoverage eg officers to sparsely crowded terminals
No adversary reactions
Multiple deployments over multiple years without us forcing them
50
59
Lab Evaluation via Simulations
Example from IRIS (FAMS)
-10
-8
-6
-4
-2
0
2
4
6
50 150 250
Defe
nde
r E
xpe
cte
d u
tilit
y
Schedule Size
Uniform Weighted random 1 Weighted random 2 IRIS
51
59
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage
Patrols Before PROTECT Boston Patrols After PROTECT Boston
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Base Patrol Area
52
PROTECT (Coast Guard) 350 increase defender expected utility
59
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage for Less Effort
53
IRIS for FAMS Outperformed expert human over six monthsReportGAO-09-903T
ARMOR at LAX Savings of up to an hour a day in scheduling
59
Field Evaluation Human vs Game Theory Competition
Counter-terrorism Patrol Scheduling
90 officers on LA Metro Trains
Humans required significant effort
Worse schedules than game theory
Observerrsquos report on questions
54
3
35
4
45
5
55
Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Q9
Q10
Q11
Q12
Secu
rity
Sco
re
Human Game Theory
59
Field Test Against Adversaries Mock Attackers
Example from PROTECT
ldquoMock attackerrdquo team deployed in Boston
Comparing PRE- to POST-PROTECT ldquodeterrencerdquo improved
Additional real-world indicators from Boston
Boston boaters questions
ldquohas the Coast Guard recently acquired more boatsrdquo
POST-PROTECT Actual reports of illegal activity
55
5956
Field Tests Against Adversaries
Computational Game Theory in the Field
Game theory vs Random
21 days of patrol
Identical conditions
Random + Human
Not controlled
0
20
40
60
80
100 Miscellaneous
Drugs
Firearm Violations
0
5
10
15
20
Captures30 min
Warnings30 min
Violations30 min
Game Theory
Rand+Human
Controlled
59
Expert Evaluation
Example from ARMOR IRIS amp PROTECT
February 2009 Commendations
LAX Police (City of Los Angeles)
July 2011 Operational Excellence
Award (US Coast Guard Boston)
September 2011 Certificate of
Appreciation (Federal Air Marshals)
June 2013 Meritorious Team Commendation
from Commandant (US Coast Guard)
57
59
Summary Security Games
Decision aids based on computational game theory in daily use
Optimize limited security resources against adversaries
Applications yield research challenges Science of security games
Scale-up Incremental strategy generation amp Marginals
Uncertainty Integrate MDPs Robustness Quantal response
Current applications (wildlife security) Interdisciplinar challenge
Global challenges Merge planninglearning amp security games
58
59
Just the Beginning of ldquoSecurity Gamesrdquohellip
Our next steps
59
Thank you
to sponsors
StartupARMORWAY Game theory in the field
Follow on research amp applications
Privacy audits
(Sinha IJCAIrsquo13)
Software testing
(Kukreja ASErsquo13)
Sport event security
(Yin AAAIrsquo14)
Singapore train
(Varakantham IAAIrsquo13)
Exam questions
(Li IJCAIrsquo13)
59
THANK YOU
tambeuscedu
httpteamcoreuscedusecurity
60
59
Uncertainty in Adversary Decision [2012]
Robust vs Modeling Adversaries
Robustness Bound loss to defender Not model attacker via QR
Defeating Robust Learned subjective utility
Robust wins Draw QR wins
42 52 6
Results on 100 games
SU-QR wins Draw Robust
13 8 1
Results on 22 games
MqxCa
qxR
ji
Xi
ij
jiij
Xi Qj
qx
)1()(0
max
β (Adversaryrsquos utility loss if
deviates from optimal) gt=
(Defenderrsquos utility loss due to
adversary deviation)
penaltyattackw
rewardattackw
probcapturewjaSEU
3
2
1)(
M
j
jxSEU
jxSEU
jadversary
adversary
e
eq
1
)(
)(
SU-QR wins Draw Robust
6 13 3
Results against security experts
With Sarit Kraus 46
5947
PAWS Protection Assistant for Wildlife Security[2014]Repeated Stackelberg Game
Simulation
Learn from crime data
Improve model
Defender calculates
mixed strategy
Defender executes
randomized patrols
Poachers attack targets
119890119878119864119880119894(w1w2w3)
119896 119890119878119864119880119896(w1w2w3)
Bayesian SUQR Heterogeneous Poachers
5948
PAWS Test April 2014
Trials in Queen Elizabeth National Park
Andrew Lemieux with rangers
on PAWS patrol in Uganda
59
Security Resource OptimizationEvaluating Deployed Security Systems Not Easy
Game theory Improvement over previous approaches
Previous Human schedulers or ldquosimple randomrdquo
49
Lab
Evaluation
Simulated
adversary
Human subject
adversaries
Field Evaluation
Patrol quality
Unpredictable Cover
Compare real schedules
Scheduling competition
Expert evaluation
Field Evaluation
Tests against adversaries
ldquoMock attackersrdquo
Capture rates of
real adversaries
59
Why Does Game Theory Perform Better
Weaknesses of Previous Methods
Human schedulers
Predictable patterns eg US Coast Guard
Scheduling effort amp cognitive burden
Simple random (eg dice roll)
Wrong weightscoverage eg officers to sparsely crowded terminals
No adversary reactions
Multiple deployments over multiple years without us forcing them
50
59
Lab Evaluation via Simulations
Example from IRIS (FAMS)
-10
-8
-6
-4
-2
0
2
4
6
50 150 250
Defe
nde
r E
xpe
cte
d u
tilit
y
Schedule Size
Uniform Weighted random 1 Weighted random 2 IRIS
51
59
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage
Patrols Before PROTECT Boston Patrols After PROTECT Boston
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Base Patrol Area
52
PROTECT (Coast Guard) 350 increase defender expected utility
59
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage for Less Effort
53
IRIS for FAMS Outperformed expert human over six monthsReportGAO-09-903T
ARMOR at LAX Savings of up to an hour a day in scheduling
59
Field Evaluation Human vs Game Theory Competition
Counter-terrorism Patrol Scheduling
90 officers on LA Metro Trains
Humans required significant effort
Worse schedules than game theory
Observerrsquos report on questions
54
3
35
4
45
5
55
Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Q9
Q10
Q11
Q12
Secu
rity
Sco
re
Human Game Theory
59
Field Test Against Adversaries Mock Attackers
Example from PROTECT
ldquoMock attackerrdquo team deployed in Boston
Comparing PRE- to POST-PROTECT ldquodeterrencerdquo improved
Additional real-world indicators from Boston
Boston boaters questions
ldquohas the Coast Guard recently acquired more boatsrdquo
POST-PROTECT Actual reports of illegal activity
55
5956
Field Tests Against Adversaries
Computational Game Theory in the Field
Game theory vs Random
21 days of patrol
Identical conditions
Random + Human
Not controlled
0
20
40
60
80
100 Miscellaneous
Drugs
Firearm Violations
0
5
10
15
20
Captures30 min
Warnings30 min
Violations30 min
Game Theory
Rand+Human
Controlled
59
Expert Evaluation
Example from ARMOR IRIS amp PROTECT
February 2009 Commendations
LAX Police (City of Los Angeles)
July 2011 Operational Excellence
Award (US Coast Guard Boston)
September 2011 Certificate of
Appreciation (Federal Air Marshals)
June 2013 Meritorious Team Commendation
from Commandant (US Coast Guard)
57
59
Summary Security Games
Decision aids based on computational game theory in daily use
Optimize limited security resources against adversaries
Applications yield research challenges Science of security games
Scale-up Incremental strategy generation amp Marginals
Uncertainty Integrate MDPs Robustness Quantal response
Current applications (wildlife security) Interdisciplinar challenge
Global challenges Merge planninglearning amp security games
58
59
Just the Beginning of ldquoSecurity Gamesrdquohellip
Our next steps
59
Thank you
to sponsors
StartupARMORWAY Game theory in the field
Follow on research amp applications
Privacy audits
(Sinha IJCAIrsquo13)
Software testing
(Kukreja ASErsquo13)
Sport event security
(Yin AAAIrsquo14)
Singapore train
(Varakantham IAAIrsquo13)
Exam questions
(Li IJCAIrsquo13)
59
THANK YOU
tambeuscedu
httpteamcoreuscedusecurity
60
5947
PAWS Protection Assistant for Wildlife Security[2014]Repeated Stackelberg Game
Simulation
Learn from crime data
Improve model
Defender calculates
mixed strategy
Defender executes
randomized patrols
Poachers attack targets
119890119878119864119880119894(w1w2w3)
119896 119890119878119864119880119896(w1w2w3)
Bayesian SUQR Heterogeneous Poachers
5948
PAWS Test April 2014
Trials in Queen Elizabeth National Park
Andrew Lemieux with rangers
on PAWS patrol in Uganda
59
Security Resource OptimizationEvaluating Deployed Security Systems Not Easy
Game theory Improvement over previous approaches
Previous Human schedulers or ldquosimple randomrdquo
49
Lab
Evaluation
Simulated
adversary
Human subject
adversaries
Field Evaluation
Patrol quality
Unpredictable Cover
Compare real schedules
Scheduling competition
Expert evaluation
Field Evaluation
Tests against adversaries
ldquoMock attackersrdquo
Capture rates of
real adversaries
59
Why Does Game Theory Perform Better
Weaknesses of Previous Methods
Human schedulers
Predictable patterns eg US Coast Guard
Scheduling effort amp cognitive burden
Simple random (eg dice roll)
Wrong weightscoverage eg officers to sparsely crowded terminals
No adversary reactions
Multiple deployments over multiple years without us forcing them
50
59
Lab Evaluation via Simulations
Example from IRIS (FAMS)
-10
-8
-6
-4
-2
0
2
4
6
50 150 250
Defe
nde
r E
xpe
cte
d u
tilit
y
Schedule Size
Uniform Weighted random 1 Weighted random 2 IRIS
51
59
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage
Patrols Before PROTECT Boston Patrols After PROTECT Boston
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Base Patrol Area
52
PROTECT (Coast Guard) 350 increase defender expected utility
59
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage for Less Effort
53
IRIS for FAMS Outperformed expert human over six monthsReportGAO-09-903T
ARMOR at LAX Savings of up to an hour a day in scheduling
59
Field Evaluation Human vs Game Theory Competition
Counter-terrorism Patrol Scheduling
90 officers on LA Metro Trains
Humans required significant effort
Worse schedules than game theory
Observerrsquos report on questions
54
3
35
4
45
5
55
Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Q9
Q10
Q11
Q12
Secu
rity
Sco
re
Human Game Theory
59
Field Test Against Adversaries Mock Attackers
Example from PROTECT
ldquoMock attackerrdquo team deployed in Boston
Comparing PRE- to POST-PROTECT ldquodeterrencerdquo improved
Additional real-world indicators from Boston
Boston boaters questions
ldquohas the Coast Guard recently acquired more boatsrdquo
POST-PROTECT Actual reports of illegal activity
55
5956
Field Tests Against Adversaries
Computational Game Theory in the Field
Game theory vs Random
21 days of patrol
Identical conditions
Random + Human
Not controlled
0
20
40
60
80
100 Miscellaneous
Drugs
Firearm Violations
0
5
10
15
20
Captures30 min
Warnings30 min
Violations30 min
Game Theory
Rand+Human
Controlled
59
Expert Evaluation
Example from ARMOR IRIS amp PROTECT
February 2009 Commendations
LAX Police (City of Los Angeles)
July 2011 Operational Excellence
Award (US Coast Guard Boston)
September 2011 Certificate of
Appreciation (Federal Air Marshals)
June 2013 Meritorious Team Commendation
from Commandant (US Coast Guard)
57
59
Summary Security Games
Decision aids based on computational game theory in daily use
Optimize limited security resources against adversaries
Applications yield research challenges Science of security games
Scale-up Incremental strategy generation amp Marginals
Uncertainty Integrate MDPs Robustness Quantal response
Current applications (wildlife security) Interdisciplinar challenge
Global challenges Merge planninglearning amp security games
58
59
Just the Beginning of ldquoSecurity Gamesrdquohellip
Our next steps
59
Thank you
to sponsors
StartupARMORWAY Game theory in the field
Follow on research amp applications
Privacy audits
(Sinha IJCAIrsquo13)
Software testing
(Kukreja ASErsquo13)
Sport event security
(Yin AAAIrsquo14)
Singapore train
(Varakantham IAAIrsquo13)
Exam questions
(Li IJCAIrsquo13)
59
THANK YOU
tambeuscedu
httpteamcoreuscedusecurity
60
5948
PAWS Test April 2014
Trials in Queen Elizabeth National Park
Andrew Lemieux with rangers
on PAWS patrol in Uganda
59
Security Resource OptimizationEvaluating Deployed Security Systems Not Easy
Game theory Improvement over previous approaches
Previous Human schedulers or ldquosimple randomrdquo
49
Lab
Evaluation
Simulated
adversary
Human subject
adversaries
Field Evaluation
Patrol quality
Unpredictable Cover
Compare real schedules
Scheduling competition
Expert evaluation
Field Evaluation
Tests against adversaries
ldquoMock attackersrdquo
Capture rates of
real adversaries
59
Why Does Game Theory Perform Better
Weaknesses of Previous Methods
Human schedulers
Predictable patterns eg US Coast Guard
Scheduling effort amp cognitive burden
Simple random (eg dice roll)
Wrong weightscoverage eg officers to sparsely crowded terminals
No adversary reactions
Multiple deployments over multiple years without us forcing them
50
59
Lab Evaluation via Simulations
Example from IRIS (FAMS)
-10
-8
-6
-4
-2
0
2
4
6
50 150 250
Defe
nde
r E
xpe
cte
d u
tilit
y
Schedule Size
Uniform Weighted random 1 Weighted random 2 IRIS
51
59
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage
Patrols Before PROTECT Boston Patrols After PROTECT Boston
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Base Patrol Area
52
PROTECT (Coast Guard) 350 increase defender expected utility
59
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage for Less Effort
53
IRIS for FAMS Outperformed expert human over six monthsReportGAO-09-903T
ARMOR at LAX Savings of up to an hour a day in scheduling
59
Field Evaluation Human vs Game Theory Competition
Counter-terrorism Patrol Scheduling
90 officers on LA Metro Trains
Humans required significant effort
Worse schedules than game theory
Observerrsquos report on questions
54
3
35
4
45
5
55
Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Q9
Q10
Q11
Q12
Secu
rity
Sco
re
Human Game Theory
59
Field Test Against Adversaries Mock Attackers
Example from PROTECT
ldquoMock attackerrdquo team deployed in Boston
Comparing PRE- to POST-PROTECT ldquodeterrencerdquo improved
Additional real-world indicators from Boston
Boston boaters questions
ldquohas the Coast Guard recently acquired more boatsrdquo
POST-PROTECT Actual reports of illegal activity
55
5956
Field Tests Against Adversaries
Computational Game Theory in the Field
Game theory vs Random
21 days of patrol
Identical conditions
Random + Human
Not controlled
0
20
40
60
80
100 Miscellaneous
Drugs
Firearm Violations
0
5
10
15
20
Captures30 min
Warnings30 min
Violations30 min
Game Theory
Rand+Human
Controlled
59
Expert Evaluation
Example from ARMOR IRIS amp PROTECT
February 2009 Commendations
LAX Police (City of Los Angeles)
July 2011 Operational Excellence
Award (US Coast Guard Boston)
September 2011 Certificate of
Appreciation (Federal Air Marshals)
June 2013 Meritorious Team Commendation
from Commandant (US Coast Guard)
57
59
Summary Security Games
Decision aids based on computational game theory in daily use
Optimize limited security resources against adversaries
Applications yield research challenges Science of security games
Scale-up Incremental strategy generation amp Marginals
Uncertainty Integrate MDPs Robustness Quantal response
Current applications (wildlife security) Interdisciplinar challenge
Global challenges Merge planninglearning amp security games
58
59
Just the Beginning of ldquoSecurity Gamesrdquohellip
Our next steps
59
Thank you
to sponsors
StartupARMORWAY Game theory in the field
Follow on research amp applications
Privacy audits
(Sinha IJCAIrsquo13)
Software testing
(Kukreja ASErsquo13)
Sport event security
(Yin AAAIrsquo14)
Singapore train
(Varakantham IAAIrsquo13)
Exam questions
(Li IJCAIrsquo13)
59
THANK YOU
tambeuscedu
httpteamcoreuscedusecurity
60
59
Security Resource OptimizationEvaluating Deployed Security Systems Not Easy
Game theory Improvement over previous approaches
Previous Human schedulers or ldquosimple randomrdquo
49
Lab
Evaluation
Simulated
adversary
Human subject
adversaries
Field Evaluation
Patrol quality
Unpredictable Cover
Compare real schedules
Scheduling competition
Expert evaluation
Field Evaluation
Tests against adversaries
ldquoMock attackersrdquo
Capture rates of
real adversaries
59
Why Does Game Theory Perform Better
Weaknesses of Previous Methods
Human schedulers
Predictable patterns eg US Coast Guard
Scheduling effort amp cognitive burden
Simple random (eg dice roll)
Wrong weightscoverage eg officers to sparsely crowded terminals
No adversary reactions
Multiple deployments over multiple years without us forcing them
50
59
Lab Evaluation via Simulations
Example from IRIS (FAMS)
-10
-8
-6
-4
-2
0
2
4
6
50 150 250
Defe
nde
r E
xpe
cte
d u
tilit
y
Schedule Size
Uniform Weighted random 1 Weighted random 2 IRIS
51
59
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage
Patrols Before PROTECT Boston Patrols After PROTECT Boston
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Base Patrol Area
52
PROTECT (Coast Guard) 350 increase defender expected utility
59
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage for Less Effort
53
IRIS for FAMS Outperformed expert human over six monthsReportGAO-09-903T
ARMOR at LAX Savings of up to an hour a day in scheduling
59
Field Evaluation Human vs Game Theory Competition
Counter-terrorism Patrol Scheduling
90 officers on LA Metro Trains
Humans required significant effort
Worse schedules than game theory
Observerrsquos report on questions
54
3
35
4
45
5
55
Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Q9
Q10
Q11
Q12
Secu
rity
Sco
re
Human Game Theory
59
Field Test Against Adversaries Mock Attackers
Example from PROTECT
ldquoMock attackerrdquo team deployed in Boston
Comparing PRE- to POST-PROTECT ldquodeterrencerdquo improved
Additional real-world indicators from Boston
Boston boaters questions
ldquohas the Coast Guard recently acquired more boatsrdquo
POST-PROTECT Actual reports of illegal activity
55
5956
Field Tests Against Adversaries
Computational Game Theory in the Field
Game theory vs Random
21 days of patrol
Identical conditions
Random + Human
Not controlled
0
20
40
60
80
100 Miscellaneous
Drugs
Firearm Violations
0
5
10
15
20
Captures30 min
Warnings30 min
Violations30 min
Game Theory
Rand+Human
Controlled
59
Expert Evaluation
Example from ARMOR IRIS amp PROTECT
February 2009 Commendations
LAX Police (City of Los Angeles)
July 2011 Operational Excellence
Award (US Coast Guard Boston)
September 2011 Certificate of
Appreciation (Federal Air Marshals)
June 2013 Meritorious Team Commendation
from Commandant (US Coast Guard)
57
59
Summary Security Games
Decision aids based on computational game theory in daily use
Optimize limited security resources against adversaries
Applications yield research challenges Science of security games
Scale-up Incremental strategy generation amp Marginals
Uncertainty Integrate MDPs Robustness Quantal response
Current applications (wildlife security) Interdisciplinar challenge
Global challenges Merge planninglearning amp security games
58
59
Just the Beginning of ldquoSecurity Gamesrdquohellip
Our next steps
59
Thank you
to sponsors
StartupARMORWAY Game theory in the field
Follow on research amp applications
Privacy audits
(Sinha IJCAIrsquo13)
Software testing
(Kukreja ASErsquo13)
Sport event security
(Yin AAAIrsquo14)
Singapore train
(Varakantham IAAIrsquo13)
Exam questions
(Li IJCAIrsquo13)
59
THANK YOU
tambeuscedu
httpteamcoreuscedusecurity
60
59
Why Does Game Theory Perform Better
Weaknesses of Previous Methods
Human schedulers
Predictable patterns eg US Coast Guard
Scheduling effort amp cognitive burden
Simple random (eg dice roll)
Wrong weightscoverage eg officers to sparsely crowded terminals
No adversary reactions
Multiple deployments over multiple years without us forcing them
50
59
Lab Evaluation via Simulations
Example from IRIS (FAMS)
-10
-8
-6
-4
-2
0
2
4
6
50 150 250
Defe
nde
r E
xpe
cte
d u
tilit
y
Schedule Size
Uniform Weighted random 1 Weighted random 2 IRIS
51
59
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage
Patrols Before PROTECT Boston Patrols After PROTECT Boston
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Base Patrol Area
52
PROTECT (Coast Guard) 350 increase defender expected utility
59
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage for Less Effort
53
IRIS for FAMS Outperformed expert human over six monthsReportGAO-09-903T
ARMOR at LAX Savings of up to an hour a day in scheduling
59
Field Evaluation Human vs Game Theory Competition
Counter-terrorism Patrol Scheduling
90 officers on LA Metro Trains
Humans required significant effort
Worse schedules than game theory
Observerrsquos report on questions
54
3
35
4
45
5
55
Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Q9
Q10
Q11
Q12
Secu
rity
Sco
re
Human Game Theory
59
Field Test Against Adversaries Mock Attackers
Example from PROTECT
ldquoMock attackerrdquo team deployed in Boston
Comparing PRE- to POST-PROTECT ldquodeterrencerdquo improved
Additional real-world indicators from Boston
Boston boaters questions
ldquohas the Coast Guard recently acquired more boatsrdquo
POST-PROTECT Actual reports of illegal activity
55
5956
Field Tests Against Adversaries
Computational Game Theory in the Field
Game theory vs Random
21 days of patrol
Identical conditions
Random + Human
Not controlled
0
20
40
60
80
100 Miscellaneous
Drugs
Firearm Violations
0
5
10
15
20
Captures30 min
Warnings30 min
Violations30 min
Game Theory
Rand+Human
Controlled
59
Expert Evaluation
Example from ARMOR IRIS amp PROTECT
February 2009 Commendations
LAX Police (City of Los Angeles)
July 2011 Operational Excellence
Award (US Coast Guard Boston)
September 2011 Certificate of
Appreciation (Federal Air Marshals)
June 2013 Meritorious Team Commendation
from Commandant (US Coast Guard)
57
59
Summary Security Games
Decision aids based on computational game theory in daily use
Optimize limited security resources against adversaries
Applications yield research challenges Science of security games
Scale-up Incremental strategy generation amp Marginals
Uncertainty Integrate MDPs Robustness Quantal response
Current applications (wildlife security) Interdisciplinar challenge
Global challenges Merge planninglearning amp security games
58
59
Just the Beginning of ldquoSecurity Gamesrdquohellip
Our next steps
59
Thank you
to sponsors
StartupARMORWAY Game theory in the field
Follow on research amp applications
Privacy audits
(Sinha IJCAIrsquo13)
Software testing
(Kukreja ASErsquo13)
Sport event security
(Yin AAAIrsquo14)
Singapore train
(Varakantham IAAIrsquo13)
Exam questions
(Li IJCAIrsquo13)
59
THANK YOU
tambeuscedu
httpteamcoreuscedusecurity
60
59
Lab Evaluation via Simulations
Example from IRIS (FAMS)
-10
-8
-6
-4
-2
0
2
4
6
50 150 250
Defe
nde
r E
xpe
cte
d u
tilit
y
Schedule Size
Uniform Weighted random 1 Weighted random 2 IRIS
51
59
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage
Patrols Before PROTECT Boston Patrols After PROTECT Boston
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Base Patrol Area
52
PROTECT (Coast Guard) 350 increase defender expected utility
59
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage for Less Effort
53
IRIS for FAMS Outperformed expert human over six monthsReportGAO-09-903T
ARMOR at LAX Savings of up to an hour a day in scheduling
59
Field Evaluation Human vs Game Theory Competition
Counter-terrorism Patrol Scheduling
90 officers on LA Metro Trains
Humans required significant effort
Worse schedules than game theory
Observerrsquos report on questions
54
3
35
4
45
5
55
Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Q9
Q10
Q11
Q12
Secu
rity
Sco
re
Human Game Theory
59
Field Test Against Adversaries Mock Attackers
Example from PROTECT
ldquoMock attackerrdquo team deployed in Boston
Comparing PRE- to POST-PROTECT ldquodeterrencerdquo improved
Additional real-world indicators from Boston
Boston boaters questions
ldquohas the Coast Guard recently acquired more boatsrdquo
POST-PROTECT Actual reports of illegal activity
55
5956
Field Tests Against Adversaries
Computational Game Theory in the Field
Game theory vs Random
21 days of patrol
Identical conditions
Random + Human
Not controlled
0
20
40
60
80
100 Miscellaneous
Drugs
Firearm Violations
0
5
10
15
20
Captures30 min
Warnings30 min
Violations30 min
Game Theory
Rand+Human
Controlled
59
Expert Evaluation
Example from ARMOR IRIS amp PROTECT
February 2009 Commendations
LAX Police (City of Los Angeles)
July 2011 Operational Excellence
Award (US Coast Guard Boston)
September 2011 Certificate of
Appreciation (Federal Air Marshals)
June 2013 Meritorious Team Commendation
from Commandant (US Coast Guard)
57
59
Summary Security Games
Decision aids based on computational game theory in daily use
Optimize limited security resources against adversaries
Applications yield research challenges Science of security games
Scale-up Incremental strategy generation amp Marginals
Uncertainty Integrate MDPs Robustness Quantal response
Current applications (wildlife security) Interdisciplinar challenge
Global challenges Merge planninglearning amp security games
58
59
Just the Beginning of ldquoSecurity Gamesrdquohellip
Our next steps
59
Thank you
to sponsors
StartupARMORWAY Game theory in the field
Follow on research amp applications
Privacy audits
(Sinha IJCAIrsquo13)
Software testing
(Kukreja ASErsquo13)
Sport event security
(Yin AAAIrsquo14)
Singapore train
(Varakantham IAAIrsquo13)
Exam questions
(Li IJCAIrsquo13)
59
THANK YOU
tambeuscedu
httpteamcoreuscedusecurity
60
59
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage
Patrols Before PROTECT Boston Patrols After PROTECT Boston
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Co
un
t
Base Patrol Area
52
PROTECT (Coast Guard) 350 increase defender expected utility
59
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage for Less Effort
53
IRIS for FAMS Outperformed expert human over six monthsReportGAO-09-903T
ARMOR at LAX Savings of up to an hour a day in scheduling
59
Field Evaluation Human vs Game Theory Competition
Counter-terrorism Patrol Scheduling
90 officers on LA Metro Trains
Humans required significant effort
Worse schedules than game theory
Observerrsquos report on questions
54
3
35
4
45
5
55
Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Q9
Q10
Q11
Q12
Secu
rity
Sco
re
Human Game Theory
59
Field Test Against Adversaries Mock Attackers
Example from PROTECT
ldquoMock attackerrdquo team deployed in Boston
Comparing PRE- to POST-PROTECT ldquodeterrencerdquo improved
Additional real-world indicators from Boston
Boston boaters questions
ldquohas the Coast Guard recently acquired more boatsrdquo
POST-PROTECT Actual reports of illegal activity
55
5956
Field Tests Against Adversaries
Computational Game Theory in the Field
Game theory vs Random
21 days of patrol
Identical conditions
Random + Human
Not controlled
0
20
40
60
80
100 Miscellaneous
Drugs
Firearm Violations
0
5
10
15
20
Captures30 min
Warnings30 min
Violations30 min
Game Theory
Rand+Human
Controlled
59
Expert Evaluation
Example from ARMOR IRIS amp PROTECT
February 2009 Commendations
LAX Police (City of Los Angeles)
July 2011 Operational Excellence
Award (US Coast Guard Boston)
September 2011 Certificate of
Appreciation (Federal Air Marshals)
June 2013 Meritorious Team Commendation
from Commandant (US Coast Guard)
57
59
Summary Security Games
Decision aids based on computational game theory in daily use
Optimize limited security resources against adversaries
Applications yield research challenges Science of security games
Scale-up Incremental strategy generation amp Marginals
Uncertainty Integrate MDPs Robustness Quantal response
Current applications (wildlife security) Interdisciplinar challenge
Global challenges Merge planninglearning amp security games
58
59
Just the Beginning of ldquoSecurity Gamesrdquohellip
Our next steps
59
Thank you
to sponsors
StartupARMORWAY Game theory in the field
Follow on research amp applications
Privacy audits
(Sinha IJCAIrsquo13)
Software testing
(Kukreja ASErsquo13)
Sport event security
(Yin AAAIrsquo14)
Singapore train
(Varakantham IAAIrsquo13)
Exam questions
(Li IJCAIrsquo13)
59
THANK YOU
tambeuscedu
httpteamcoreuscedusecurity
60
59
Field Evaluation of Schedule Quality
Improved Patrol Unpredictability amp Coverage for Less Effort
53
IRIS for FAMS Outperformed expert human over six monthsReportGAO-09-903T
ARMOR at LAX Savings of up to an hour a day in scheduling
59
Field Evaluation Human vs Game Theory Competition
Counter-terrorism Patrol Scheduling
90 officers on LA Metro Trains
Humans required significant effort
Worse schedules than game theory
Observerrsquos report on questions
54
3
35
4
45
5
55
Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Q9
Q10
Q11
Q12
Secu
rity
Sco
re
Human Game Theory
59
Field Test Against Adversaries Mock Attackers
Example from PROTECT
ldquoMock attackerrdquo team deployed in Boston
Comparing PRE- to POST-PROTECT ldquodeterrencerdquo improved
Additional real-world indicators from Boston
Boston boaters questions
ldquohas the Coast Guard recently acquired more boatsrdquo
POST-PROTECT Actual reports of illegal activity
55
5956
Field Tests Against Adversaries
Computational Game Theory in the Field
Game theory vs Random
21 days of patrol
Identical conditions
Random + Human
Not controlled
0
20
40
60
80
100 Miscellaneous
Drugs
Firearm Violations
0
5
10
15
20
Captures30 min
Warnings30 min
Violations30 min
Game Theory
Rand+Human
Controlled
59
Expert Evaluation
Example from ARMOR IRIS amp PROTECT
February 2009 Commendations
LAX Police (City of Los Angeles)
July 2011 Operational Excellence
Award (US Coast Guard Boston)
September 2011 Certificate of
Appreciation (Federal Air Marshals)
June 2013 Meritorious Team Commendation
from Commandant (US Coast Guard)
57
59
Summary Security Games
Decision aids based on computational game theory in daily use
Optimize limited security resources against adversaries
Applications yield research challenges Science of security games
Scale-up Incremental strategy generation amp Marginals
Uncertainty Integrate MDPs Robustness Quantal response
Current applications (wildlife security) Interdisciplinar challenge
Global challenges Merge planninglearning amp security games
58
59
Just the Beginning of ldquoSecurity Gamesrdquohellip
Our next steps
59
Thank you
to sponsors
StartupARMORWAY Game theory in the field
Follow on research amp applications
Privacy audits
(Sinha IJCAIrsquo13)
Software testing
(Kukreja ASErsquo13)
Sport event security
(Yin AAAIrsquo14)
Singapore train
(Varakantham IAAIrsquo13)
Exam questions
(Li IJCAIrsquo13)
59
THANK YOU
tambeuscedu
httpteamcoreuscedusecurity
60
59
Field Evaluation Human vs Game Theory Competition
Counter-terrorism Patrol Scheduling
90 officers on LA Metro Trains
Humans required significant effort
Worse schedules than game theory
Observerrsquos report on questions
54
3
35
4
45
5
55
Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Q9
Q10
Q11
Q12
Secu
rity
Sco
re
Human Game Theory
59
Field Test Against Adversaries Mock Attackers
Example from PROTECT
ldquoMock attackerrdquo team deployed in Boston
Comparing PRE- to POST-PROTECT ldquodeterrencerdquo improved
Additional real-world indicators from Boston
Boston boaters questions
ldquohas the Coast Guard recently acquired more boatsrdquo
POST-PROTECT Actual reports of illegal activity
55
5956
Field Tests Against Adversaries
Computational Game Theory in the Field
Game theory vs Random
21 days of patrol
Identical conditions
Random + Human
Not controlled
0
20
40
60
80
100 Miscellaneous
Drugs
Firearm Violations
0
5
10
15
20
Captures30 min
Warnings30 min
Violations30 min
Game Theory
Rand+Human
Controlled
59
Expert Evaluation
Example from ARMOR IRIS amp PROTECT
February 2009 Commendations
LAX Police (City of Los Angeles)
July 2011 Operational Excellence
Award (US Coast Guard Boston)
September 2011 Certificate of
Appreciation (Federal Air Marshals)
June 2013 Meritorious Team Commendation
from Commandant (US Coast Guard)
57
59
Summary Security Games
Decision aids based on computational game theory in daily use
Optimize limited security resources against adversaries
Applications yield research challenges Science of security games
Scale-up Incremental strategy generation amp Marginals
Uncertainty Integrate MDPs Robustness Quantal response
Current applications (wildlife security) Interdisciplinar challenge
Global challenges Merge planninglearning amp security games
58
59
Just the Beginning of ldquoSecurity Gamesrdquohellip
Our next steps
59
Thank you
to sponsors
StartupARMORWAY Game theory in the field
Follow on research amp applications
Privacy audits
(Sinha IJCAIrsquo13)
Software testing
(Kukreja ASErsquo13)
Sport event security
(Yin AAAIrsquo14)
Singapore train
(Varakantham IAAIrsquo13)
Exam questions
(Li IJCAIrsquo13)
59
THANK YOU
tambeuscedu
httpteamcoreuscedusecurity
60
59
Field Test Against Adversaries Mock Attackers
Example from PROTECT
ldquoMock attackerrdquo team deployed in Boston
Comparing PRE- to POST-PROTECT ldquodeterrencerdquo improved
Additional real-world indicators from Boston
Boston boaters questions
ldquohas the Coast Guard recently acquired more boatsrdquo
POST-PROTECT Actual reports of illegal activity
55
5956
Field Tests Against Adversaries
Computational Game Theory in the Field
Game theory vs Random
21 days of patrol
Identical conditions
Random + Human
Not controlled
0
20
40
60
80
100 Miscellaneous
Drugs
Firearm Violations
0
5
10
15
20
Captures30 min
Warnings30 min
Violations30 min
Game Theory
Rand+Human
Controlled
59
Expert Evaluation
Example from ARMOR IRIS amp PROTECT
February 2009 Commendations
LAX Police (City of Los Angeles)
July 2011 Operational Excellence
Award (US Coast Guard Boston)
September 2011 Certificate of
Appreciation (Federal Air Marshals)
June 2013 Meritorious Team Commendation
from Commandant (US Coast Guard)
57
59
Summary Security Games
Decision aids based on computational game theory in daily use
Optimize limited security resources against adversaries
Applications yield research challenges Science of security games
Scale-up Incremental strategy generation amp Marginals
Uncertainty Integrate MDPs Robustness Quantal response
Current applications (wildlife security) Interdisciplinar challenge
Global challenges Merge planninglearning amp security games
58
59
Just the Beginning of ldquoSecurity Gamesrdquohellip
Our next steps
59
Thank you
to sponsors
StartupARMORWAY Game theory in the field
Follow on research amp applications
Privacy audits
(Sinha IJCAIrsquo13)
Software testing
(Kukreja ASErsquo13)
Sport event security
(Yin AAAIrsquo14)
Singapore train
(Varakantham IAAIrsquo13)
Exam questions
(Li IJCAIrsquo13)
59
THANK YOU
tambeuscedu
httpteamcoreuscedusecurity
60
5956
Field Tests Against Adversaries
Computational Game Theory in the Field
Game theory vs Random
21 days of patrol
Identical conditions
Random + Human
Not controlled
0
20
40
60
80
100 Miscellaneous
Drugs
Firearm Violations
0
5
10
15
20
Captures30 min
Warnings30 min
Violations30 min
Game Theory
Rand+Human
Controlled
59
Expert Evaluation
Example from ARMOR IRIS amp PROTECT
February 2009 Commendations
LAX Police (City of Los Angeles)
July 2011 Operational Excellence
Award (US Coast Guard Boston)
September 2011 Certificate of
Appreciation (Federal Air Marshals)
June 2013 Meritorious Team Commendation
from Commandant (US Coast Guard)
57
59
Summary Security Games
Decision aids based on computational game theory in daily use
Optimize limited security resources against adversaries
Applications yield research challenges Science of security games
Scale-up Incremental strategy generation amp Marginals
Uncertainty Integrate MDPs Robustness Quantal response
Current applications (wildlife security) Interdisciplinar challenge
Global challenges Merge planninglearning amp security games
58
59
Just the Beginning of ldquoSecurity Gamesrdquohellip
Our next steps
59
Thank you
to sponsors
StartupARMORWAY Game theory in the field
Follow on research amp applications
Privacy audits
(Sinha IJCAIrsquo13)
Software testing
(Kukreja ASErsquo13)
Sport event security
(Yin AAAIrsquo14)
Singapore train
(Varakantham IAAIrsquo13)
Exam questions
(Li IJCAIrsquo13)
59
THANK YOU
tambeuscedu
httpteamcoreuscedusecurity
60
59
Expert Evaluation
Example from ARMOR IRIS amp PROTECT
February 2009 Commendations
LAX Police (City of Los Angeles)
July 2011 Operational Excellence
Award (US Coast Guard Boston)
September 2011 Certificate of
Appreciation (Federal Air Marshals)
June 2013 Meritorious Team Commendation
from Commandant (US Coast Guard)
57
59
Summary Security Games
Decision aids based on computational game theory in daily use
Optimize limited security resources against adversaries
Applications yield research challenges Science of security games
Scale-up Incremental strategy generation amp Marginals
Uncertainty Integrate MDPs Robustness Quantal response
Current applications (wildlife security) Interdisciplinar challenge
Global challenges Merge planninglearning amp security games
58
59
Just the Beginning of ldquoSecurity Gamesrdquohellip
Our next steps
59
Thank you
to sponsors
StartupARMORWAY Game theory in the field
Follow on research amp applications
Privacy audits
(Sinha IJCAIrsquo13)
Software testing
(Kukreja ASErsquo13)
Sport event security
(Yin AAAIrsquo14)
Singapore train
(Varakantham IAAIrsquo13)
Exam questions
(Li IJCAIrsquo13)
59
THANK YOU
tambeuscedu
httpteamcoreuscedusecurity
60
59
Summary Security Games
Decision aids based on computational game theory in daily use
Optimize limited security resources against adversaries
Applications yield research challenges Science of security games
Scale-up Incremental strategy generation amp Marginals
Uncertainty Integrate MDPs Robustness Quantal response
Current applications (wildlife security) Interdisciplinar challenge
Global challenges Merge planninglearning amp security games
58
59
Just the Beginning of ldquoSecurity Gamesrdquohellip
Our next steps
59
Thank you
to sponsors
StartupARMORWAY Game theory in the field
Follow on research amp applications
Privacy audits
(Sinha IJCAIrsquo13)
Software testing
(Kukreja ASErsquo13)
Sport event security
(Yin AAAIrsquo14)
Singapore train
(Varakantham IAAIrsquo13)
Exam questions
(Li IJCAIrsquo13)
59
THANK YOU
tambeuscedu
httpteamcoreuscedusecurity
60
59
Just the Beginning of ldquoSecurity Gamesrdquohellip
Our next steps
59
Thank you
to sponsors
StartupARMORWAY Game theory in the field
Follow on research amp applications
Privacy audits
(Sinha IJCAIrsquo13)
Software testing
(Kukreja ASErsquo13)
Sport event security
(Yin AAAIrsquo14)
Singapore train
(Varakantham IAAIrsquo13)
Exam questions
(Li IJCAIrsquo13)
59
THANK YOU
tambeuscedu
httpteamcoreuscedusecurity
60
59
THANK YOU
tambeuscedu
httpteamcoreuscedusecurity
60