Verifying Keys through Publicity andCommunities of Trust 13

Eric Osterweil13 Dan Massey13

Danny McPherson13 Lixia Zhang13

       

 

     

 

DNSSEC Security for a Core Internet System13

bull DNS is a staple of todayrsquos online activities13 bull Is there a pedestrian online activity that doesnrsquot use DNS13 bull We use it to map unique names to network resources13 bull It has long been a very robust system13

bull DNSSEC makes DNS the first core Internet system toprotect itself and its data with hierarchical crypto13 bull Protects DNS from cache poisoning and spoofing13 bull 2010-2011 root and net and com deployed DNSSEC13 bull A straightforward design crypto-enhanced systems design13

bull The deployment has been growing and standards arebeing built on DNSSEC DANE (TLS SMIME etc)13

Verisign Public 13 2 13

Motivations Grow the Deployment (Graph From SecSpider)13

Verisign Public 13 3 13

Today we need a log-scale view ) httpsecspiderverisignlabscomgrowthhtml 13

Verisign Public 13 4 13

       

 

     

 

DNSSEC Security for a Core Internet System13

bull DNS is a staple of todayrsquos online activities13 bull Is there a pedestrian online activity that doesnrsquot use DNS13 bull We use it to map unique names to network resources13 bull It has long been a very robust system13

bull DNSSEC makes DNS the first core Internet system toprotect itself and its data with hierarchical crypto13 bull Protects DNS from cache poisoning and spoofing13 bull 2010-2011 root and net and com deployed DNSSEC13 bull A straightforward design crypto-enhanced systems design13

bull The deployment has been growing and standards arebeing built on DNSSEC DANE (TLS SMIME etc)13

Verisign Public 13 2 13

Motivations Grow the Deployment (Graph From SecSpider)13

Verisign Public 13 3 13

Today we need a log-scale view ) httpsecspiderverisignlabscomgrowthhtml 13

Verisign Public 13 4 13

Motivations Grow the Deployment (Graph From SecSpider)13

Verisign Public 13 3 13

Today we need a log-scale view ) httpsecspiderverisignlabscomgrowthhtml 13

Verisign Public 13 4 13

Today we need a log-scale view ) httpsecspiderverisignlabscomgrowthhtml 13

Verisign Public 13 4 13

 

 

     

   

 

Some Challenges for DNSSEC Remain13

bull DNSSECrsquos early life has shown some stability concerns13 bull Wersquove already seen broken delegations (gov arpa fr)13

bull DNSSEC faces architectural misalignments13 bull Looking up unique names ne Verification of public keys13 bull The design struggles with misconfigurations and partial

deployment (though this may not be unique to DNSSEC)13

bull DNS is a core staple and outages are not OK13 bull If someone puts the wrong DS record in their zone is that

game over13 bull Network partitioning can break online delegations13

Verisign Public 13 5 13

 

 

 

 

   

Some Core Questions13

bull Is black and white verification the only option for dynamic Internet-scale systems like DNS13 bull DNS has thrived because its design tolerates failures and

misconfigurations13

bull What kind of verification can one derive for Internet-scale systems with dynamism like this13 bull Such a verification system must tolerate the Internetrsquos chaotic

setting13

bull Can any other verification model that is based on sucha shaky operational foundation be trustworthy13 bull Moreover can it be better than what we have now13

Verisign Public 13 6 13

 

    

 

 

We Propose to Verify Using the Networkhellip Public Dataand Communities of Trust13

bull Add distributed redundant measurements form independent paths as a new security substrate13 bull Redundancy can overcome errors 13 bull Publicity increases verifiability13 bull Who to trust is subjective13

bull We propose the theoretical model Public Data to augment DNSSECrsquos crypto substrate13

bull We implemented a candidate system called Vantages to demonstrate its feasibility13

Verisign Public 13 7 13

 

 

 

 

Outline13

bull DNSSEC background13

bull Public Data model and Vantages13

bull Measurements13

bull Conclusion13

Verisign Public 13 8 13

 

 

 

 

 

 

DNSSEC Crypto Key Learning + Verification13

bull First attempt to enhancecore Internet system with crypto13

bull DNSSEC zones create publicprivate keys13 bull Public key is DNSKEY13

bull Zones sign all RRsets and resolvers use DNSKEYs to verify them13 bull Each RRset has a signature attached to it RRSIG13

bull Resolvers are configured with a single root key and trust flows recursively down the hierarchy13

Verisign Public 13 9 13

Using a zonersquos key13 on a standard RRset 13 (the NS)13

Signature (RRSIG) will only verify with the13 DNSKEY if no

Data Signing Example13

data was modified13

Verisign Public 13 10 13

 

 

   

 

 

Getting the Keys13

bull Until a resolver gets DNSKEY(s) data can be spoofed13

bull Keys verified by secure delegations from parents to children13 bull So resolvers know DNSKEYs are not being spoofed13

bull DNSSECrsquos design needs the full hierarchy in orderto verify keys13 bull No middle ground either a key has a verifiable delegation or

you know nothing about it13 bull What if we just queried for crypto keys directly13

Verisign Public 13 11 13

 

 

 

   

       

Public Data = Distributed polling + structured observations13

bull Verify DNSKEYs through Communities of Trust (CoTs)13 bull Consistency and

redundancy become the verification metric13

bull The network topologically diverse vantages13 bull G = (V E)13 bull V = v0 v1 hellip vn13

bull Observations bind data to time and a network path13 bull Path σ(ij) = (vi hellip vj)13 bull Data (such as a DNSKEY) d13 bull Observation oi = (dj t σ(ij))13

Verisign Public 13 12 13

       

Public Data Model13 Path = σ13

i

Name Server Svj

Resolver vi

Public Data pd

KEY

Data di

KEY

Observation oi

Message mi

bull pdi = (ditk)13 bull Svj = pd0 pdm 13 bull m = (di SigK(di)) 13 bull hellip13

Verisign Public 13 13 13

   

   

Peer-to-Peer CoTs13

bull P2P CoTs Compartmentalize13 bull CoTs are manual13

bull Trust must be bootstrapped13 bull Observed data is signed by PGP key13

14 13Verisign Public 13

v

v

v

v

v

v

v

v v v

v

v

CoT 1

CoT 2 CoT 3

 

 

 

 

 

 

Threat Model for Key Learning Man in the Middle13

bull To attack Eve must see keys that are in transit 13 bull If she must own a

vantage ve in σ13 bull But she canrsquot

arbitrarily attack just anyone13 bull Attacks between a

resolver v and ai zonersquos name servers (VZ)13

bull Not a reduction of scope this is dictated by the nature of DNS13

bull Eve must expend a real cost to own these vantages13

Verisign Public 13 15 13

 

 

 

 

What Eve Really Needs to Do13

B

C

A

D

E

F

H

G

N

M

L I

J

K

bull To spoof Eve must be in the right place at the right time13 bull She must be able to

intercept responses from all (or most) name servers13

bull The minimum set size for Ve to cut Alice off from the zone will be the min-cut set Vcut = MinCut(vi VZ)13 bull This is the lower bound on Eversquos acquisition cost13

Verisign Public 13 16 13

 

     

 

   

Security Analysis Attack Cost13

bull Eve must own vantage points (Ve) and be able to use them 13 13Acquisition + usage costs13

bull Acquisition ca(Ve) can specific nodes even be purchased13 bull Core routers at ATampT may not be on sale like grandmarsquos PC is13 bull Eve may have to get her hands dirty (if shersquos able to)13

bull Usage cu(Ve t) nodes in Ve may cost per hour or may get reclaimed if detected13 bull If renting nodes then snooping is a function of rent13 bull If Eve acquires her own nodes operators may notice her13

13 13 13C(Ve t) = ca (Ve) + cu (Ve t)13

Verisign Public 13 17 13

 

 

 

       

Acquisition Cost The Cat and Mouse Game13

bull Alicersquos best defense is to make her CoT as large andtopologically diverse as possible13

bull Eve needs to know Alicersquos CoT (and all paths to VZrsquos name servers)13 bull Note knowing any AS path is an open challenge [1]13

bull We evaluate three example types of adversaries13 1 General does not know any path info13 2 Targeted knows Alicersquos path to VZ but not her CoTrsquos13 3 Nation State will try to compromise the largest ISPs first13

[1] Mao Z M Qiu L Wang J and Zhang Y 2005 On AS-level pathinference 2005 ACM SIGMETRICS13

Verisign Public 13 18 13

 

 

 

Eversquos Probability of Success13

bull General the probability that Eve can subvert Alicersquos min-cut set is (where n is the size of Ve)13

bull Targeted as Alice augments her min-cut set theprobability of compromise approaches the General case13

bull Nation State the adversary is not focused on Alicersquos CoT but Alicersquos chances are still augmented as sheincreases her min-cut set13

Verisign Public 13 19 13

 

   

 

 

   

  

 

Evaluation13

bull Simulated an AS-level topology using the Inet topology generator13 bull Simulate 22000 ASes13

bull Chose random ASes as VZ nodes and VCoT nodes13 bull Calculated min-cut set for VZ and VCoT combinations ranging

from 2-1113 bull Used shortest path routing metric to represent routing13

bull Also deployed actual Vantages CoT13 bull Vantages written in C++ with SQLite backed DB uses GPG to

verify witness communications13 bull httpwwwvantage-pointsorg13

bull Constantly automatically learns zones and polls13 bull Aligns costs with benefits verification aligns with needs13

Verisign Public 13 20 13

 

 

 

 

   

Some Core Questions13

bull Is black and white verification the only option for dynamic Internet-scale systems like DNS13 bull DNS has thrived because its design tolerates failures and

misconfigurations13

bull What kind of verification can one derive for Internet-scale systems with dynamism like this13 bull Such a verification system must tolerate the Internetrsquos chaotic

setting13

bull Can any other verification model that is based on sucha shaky operational foundation be trustworthy13 bull Moreover can it be better than what we have now13

Verisign Public 13 6 13

 

    

 

 

We Propose to Verify Using the Networkhellip Public Dataand Communities of Trust13

bull Add distributed redundant measurements form independent paths as a new security substrate13 bull Redundancy can overcome errors 13 bull Publicity increases verifiability13 bull Who to trust is subjective13

bull We propose the theoretical model Public Data to augment DNSSECrsquos crypto substrate13

bull We implemented a candidate system called Vantages to demonstrate its feasibility13

Verisign Public 13 7 13

 

 

 

 

Outline13

bull DNSSEC background13

bull Public Data model and Vantages13

bull Measurements13

bull Conclusion13

Verisign Public 13 8 13

 

 

 

 

 

 

DNSSEC Crypto Key Learning + Verification13

bull First attempt to enhancecore Internet system with crypto13

bull DNSSEC zones create publicprivate keys13 bull Public key is DNSKEY13

bull Zones sign all RRsets and resolvers use DNSKEYs to verify them13 bull Each RRset has a signature attached to it RRSIG13

bull Resolvers are configured with a single root key and trust flows recursively down the hierarchy13

Verisign Public 13 9 13

Using a zonersquos key13 on a standard RRset 13 (the NS)13

Signature (RRSIG) will only verify with the13 DNSKEY if no

Data Signing Example13

data was modified13

Verisign Public 13 10 13

 

 

   

 

 

Getting the Keys13

bull Until a resolver gets DNSKEY(s) data can be spoofed13

bull Keys verified by secure delegations from parents to children13 bull So resolvers know DNSKEYs are not being spoofed13

bull DNSSECrsquos design needs the full hierarchy in orderto verify keys13 bull No middle ground either a key has a verifiable delegation or

you know nothing about it13 bull What if we just queried for crypto keys directly13

Verisign Public 13 11 13

 

 

 

   

       

Public Data = Distributed polling + structured observations13

bull Verify DNSKEYs through Communities of Trust (CoTs)13 bull Consistency and

redundancy become the verification metric13

bull The network topologically diverse vantages13 bull G = (V E)13 bull V = v0 v1 hellip vn13

bull Observations bind data to time and a network path13 bull Path σ(ij) = (vi hellip vj)13 bull Data (such as a DNSKEY) d13 bull Observation oi = (dj t σ(ij))13

Verisign Public 13 12 13

       

Public Data Model13 Path = σ13

i

Name Server Svj

Resolver vi

Public Data pd

KEY

Data di

KEY

Observation oi

Message mi

bull pdi = (ditk)13 bull Svj = pd0 pdm 13 bull m = (di SigK(di)) 13 bull hellip13

Verisign Public 13 13 13

   

   

Peer-to-Peer CoTs13

bull P2P CoTs Compartmentalize13 bull CoTs are manual13

bull Trust must be bootstrapped13 bull Observed data is signed by PGP key13

14 13Verisign Public 13

v

v

v

v

v

v

v

v v v

v

v

CoT 1

CoT 2 CoT 3

 

 

 

 

 

 

Threat Model for Key Learning Man in the Middle13

bull To attack Eve must see keys that are in transit 13 bull If she must own a

vantage ve in σ13 bull But she canrsquot

arbitrarily attack just anyone13 bull Attacks between a

resolver v and ai zonersquos name servers (VZ)13

bull Not a reduction of scope this is dictated by the nature of DNS13

bull Eve must expend a real cost to own these vantages13

Verisign Public 13 15 13

 

 

 

 

What Eve Really Needs to Do13

B

C

A

D

E

F

H

G

N

M

L I

J

K

bull To spoof Eve must be in the right place at the right time13 bull She must be able to

intercept responses from all (or most) name servers13

bull The minimum set size for Ve to cut Alice off from the zone will be the min-cut set Vcut = MinCut(vi VZ)13 bull This is the lower bound on Eversquos acquisition cost13

Verisign Public 13 16 13

 

     

 

   

Security Analysis Attack Cost13

bull Eve must own vantage points (Ve) and be able to use them 13 13Acquisition + usage costs13

bull Acquisition ca(Ve) can specific nodes even be purchased13 bull Core routers at ATampT may not be on sale like grandmarsquos PC is13 bull Eve may have to get her hands dirty (if shersquos able to)13

bull Usage cu(Ve t) nodes in Ve may cost per hour or may get reclaimed if detected13 bull If renting nodes then snooping is a function of rent13 bull If Eve acquires her own nodes operators may notice her13

13 13 13C(Ve t) = ca (Ve) + cu (Ve t)13

Verisign Public 13 17 13

 

 

 

       

Acquisition Cost The Cat and Mouse Game13

bull Alicersquos best defense is to make her CoT as large andtopologically diverse as possible13

bull Eve needs to know Alicersquos CoT (and all paths to VZrsquos name servers)13 bull Note knowing any AS path is an open challenge [1]13

bull We evaluate three example types of adversaries13 1 General does not know any path info13 2 Targeted knows Alicersquos path to VZ but not her CoTrsquos13 3 Nation State will try to compromise the largest ISPs first13

[1] Mao Z M Qiu L Wang J and Zhang Y 2005 On AS-level pathinference 2005 ACM SIGMETRICS13

Verisign Public 13 18 13

 

 

 

Eversquos Probability of Success13

bull General the probability that Eve can subvert Alicersquos min-cut set is (where n is the size of Ve)13

bull Targeted as Alice augments her min-cut set theprobability of compromise approaches the General case13

bull Nation State the adversary is not focused on Alicersquos CoT but Alicersquos chances are still augmented as sheincreases her min-cut set13

Verisign Public 13 19 13

 

   

 

 

   

  

 

Evaluation13

bull Simulated an AS-level topology using the Inet topology generator13 bull Simulate 22000 ASes13

bull Chose random ASes as VZ nodes and VCoT nodes13 bull Calculated min-cut set for VZ and VCoT combinations ranging

from 2-1113 bull Used shortest path routing metric to represent routing13

bull Also deployed actual Vantages CoT13 bull Vantages written in C++ with SQLite backed DB uses GPG to

verify witness communications13 bull httpwwwvantage-pointsorg13

bull Constantly automatically learns zones and polls13 bull Aligns costs with benefits verification aligns with needs13

Verisign Public 13 20 13

 

    

 

 

We Propose to Verify Using the Networkhellip Public Dataand Communities of Trust13

bull Add distributed redundant measurements form independent paths as a new security substrate13 bull Redundancy can overcome errors 13 bull Publicity increases verifiability13 bull Who to trust is subjective13

bull We propose the theoretical model Public Data to augment DNSSECrsquos crypto substrate13

bull We implemented a candidate system called Vantages to demonstrate its feasibility13

Verisign Public 13 7 13

 

 

 

 

Outline13

bull DNSSEC background13

bull Public Data model and Vantages13

bull Measurements13

bull Conclusion13

Verisign Public 13 8 13

 

 

 

 

 

 

DNSSEC Crypto Key Learning + Verification13

bull First attempt to enhancecore Internet system with crypto13

bull DNSSEC zones create publicprivate keys13 bull Public key is DNSKEY13

bull Zones sign all RRsets and resolvers use DNSKEYs to verify them13 bull Each RRset has a signature attached to it RRSIG13

bull Resolvers are configured with a single root key and trust flows recursively down the hierarchy13

Verisign Public 13 9 13

Using a zonersquos key13 on a standard RRset 13 (the NS)13

Signature (RRSIG) will only verify with the13 DNSKEY if no

Data Signing Example13

data was modified13

Verisign Public 13 10 13

 

 

   

 

 

Getting the Keys13

bull Until a resolver gets DNSKEY(s) data can be spoofed13

bull Keys verified by secure delegations from parents to children13 bull So resolvers know DNSKEYs are not being spoofed13

bull DNSSECrsquos design needs the full hierarchy in orderto verify keys13 bull No middle ground either a key has a verifiable delegation or

you know nothing about it13 bull What if we just queried for crypto keys directly13

Verisign Public 13 11 13

 

 

 

   

       

Public Data = Distributed polling + structured observations13

bull Verify DNSKEYs through Communities of Trust (CoTs)13 bull Consistency and

redundancy become the verification metric13

bull The network topologically diverse vantages13 bull G = (V E)13 bull V = v0 v1 hellip vn13

bull Observations bind data to time and a network path13 bull Path σ(ij) = (vi hellip vj)13 bull Data (such as a DNSKEY) d13 bull Observation oi = (dj t σ(ij))13

Verisign Public 13 12 13

       

Public Data Model13 Path = σ13

i

Name Server Svj

Resolver vi

Public Data pd

KEY

Data di

KEY

Observation oi

Message mi

bull pdi = (ditk)13 bull Svj = pd0 pdm 13 bull m = (di SigK(di)) 13 bull hellip13

Verisign Public 13 13 13

   

   

Peer-to-Peer CoTs13

bull P2P CoTs Compartmentalize13 bull CoTs are manual13

bull Trust must be bootstrapped13 bull Observed data is signed by PGP key13

14 13Verisign Public 13

v

v

v

v

v

v

v

v v v

v

v

CoT 1

CoT 2 CoT 3

 

 

 

 

 

 

Threat Model for Key Learning Man in the Middle13

bull To attack Eve must see keys that are in transit 13 bull If she must own a

vantage ve in σ13 bull But she canrsquot

arbitrarily attack just anyone13 bull Attacks between a

resolver v and ai zonersquos name servers (VZ)13

bull Not a reduction of scope this is dictated by the nature of DNS13

bull Eve must expend a real cost to own these vantages13

Verisign Public 13 15 13

 

 

 

 

What Eve Really Needs to Do13

B

C

A

D

E

F

H

G

N

M

L I

J

K

bull To spoof Eve must be in the right place at the right time13 bull She must be able to

intercept responses from all (or most) name servers13

bull The minimum set size for Ve to cut Alice off from the zone will be the min-cut set Vcut = MinCut(vi VZ)13 bull This is the lower bound on Eversquos acquisition cost13

Verisign Public 13 16 13

 

     

 

   

Security Analysis Attack Cost13

bull Eve must own vantage points (Ve) and be able to use them 13 13Acquisition + usage costs13

bull Acquisition ca(Ve) can specific nodes even be purchased13 bull Core routers at ATampT may not be on sale like grandmarsquos PC is13 bull Eve may have to get her hands dirty (if shersquos able to)13

bull Usage cu(Ve t) nodes in Ve may cost per hour or may get reclaimed if detected13 bull If renting nodes then snooping is a function of rent13 bull If Eve acquires her own nodes operators may notice her13

13 13 13C(Ve t) = ca (Ve) + cu (Ve t)13

Verisign Public 13 17 13

 

 

 

       

Acquisition Cost The Cat and Mouse Game13

bull Alicersquos best defense is to make her CoT as large andtopologically diverse as possible13

bull Eve needs to know Alicersquos CoT (and all paths to VZrsquos name servers)13 bull Note knowing any AS path is an open challenge [1]13

bull We evaluate three example types of adversaries13 1 General does not know any path info13 2 Targeted knows Alicersquos path to VZ but not her CoTrsquos13 3 Nation State will try to compromise the largest ISPs first13

[1] Mao Z M Qiu L Wang J and Zhang Y 2005 On AS-level pathinference 2005 ACM SIGMETRICS13

Verisign Public 13 18 13

 

 

 

Eversquos Probability of Success13

bull General the probability that Eve can subvert Alicersquos min-cut set is (where n is the size of Ve)13

bull Targeted as Alice augments her min-cut set theprobability of compromise approaches the General case13

bull Nation State the adversary is not focused on Alicersquos CoT but Alicersquos chances are still augmented as sheincreases her min-cut set13

Verisign Public 13 19 13

 

   

 

 

   

  

 

Evaluation13

bull Simulated an AS-level topology using the Inet topology generator13 bull Simulate 22000 ASes13

bull Chose random ASes as VZ nodes and VCoT nodes13 bull Calculated min-cut set for VZ and VCoT combinations ranging

from 2-1113 bull Used shortest path routing metric to represent routing13

bull Also deployed actual Vantages CoT13 bull Vantages written in C++ with SQLite backed DB uses GPG to

verify witness communications13 bull httpwwwvantage-pointsorg13

bull Constantly automatically learns zones and polls13 bull Aligns costs with benefits verification aligns with needs13

Verisign Public 13 20 13

 

 

 

 

Outline13

bull DNSSEC background13

bull Public Data model and Vantages13

bull Measurements13

bull Conclusion13

Verisign Public 13 8 13

 

 

 

 

 

 

DNSSEC Crypto Key Learning + Verification13

bull First attempt to enhancecore Internet system with crypto13

bull DNSSEC zones create publicprivate keys13 bull Public key is DNSKEY13

bull Zones sign all RRsets and resolvers use DNSKEYs to verify them13 bull Each RRset has a signature attached to it RRSIG13

bull Resolvers are configured with a single root key and trust flows recursively down the hierarchy13

Verisign Public 13 9 13

Using a zonersquos key13 on a standard RRset 13 (the NS)13

Signature (RRSIG) will only verify with the13 DNSKEY if no

Data Signing Example13

data was modified13

Verisign Public 13 10 13

 

 

   

 

 

Getting the Keys13

bull Until a resolver gets DNSKEY(s) data can be spoofed13

bull Keys verified by secure delegations from parents to children13 bull So resolvers know DNSKEYs are not being spoofed13

bull DNSSECrsquos design needs the full hierarchy in orderto verify keys13 bull No middle ground either a key has a verifiable delegation or

you know nothing about it13 bull What if we just queried for crypto keys directly13

Verisign Public 13 11 13

 

 

 

   

       

Public Data = Distributed polling + structured observations13

bull Verify DNSKEYs through Communities of Trust (CoTs)13 bull Consistency and

redundancy become the verification metric13

bull The network topologically diverse vantages13 bull G = (V E)13 bull V = v0 v1 hellip vn13

bull Observations bind data to time and a network path13 bull Path σ(ij) = (vi hellip vj)13 bull Data (such as a DNSKEY) d13 bull Observation oi = (dj t σ(ij))13

Verisign Public 13 12 13

       

Public Data Model13 Path = σ13

i

Name Server Svj

Resolver vi

Public Data pd

KEY

Data di

KEY

Observation oi

Message mi

bull pdi = (ditk)13 bull Svj = pd0 pdm 13 bull m = (di SigK(di)) 13 bull hellip13

Verisign Public 13 13 13

   

   

Peer-to-Peer CoTs13

bull P2P CoTs Compartmentalize13 bull CoTs are manual13

bull Trust must be bootstrapped13 bull Observed data is signed by PGP key13

14 13Verisign Public 13

v

v

v

v

v

v

v

v v v

v

v

CoT 1

CoT 2 CoT 3

 

 

 

 

 

 

Threat Model for Key Learning Man in the Middle13

bull To attack Eve must see keys that are in transit 13 bull If she must own a

vantage ve in σ13 bull But she canrsquot

arbitrarily attack just anyone13 bull Attacks between a

resolver v and ai zonersquos name servers (VZ)13

bull Not a reduction of scope this is dictated by the nature of DNS13

bull Eve must expend a real cost to own these vantages13

Verisign Public 13 15 13

 

 

 

 

What Eve Really Needs to Do13

B

C

A

D

E

F

H

G

N

M

L I

J

K

bull To spoof Eve must be in the right place at the right time13 bull She must be able to

intercept responses from all (or most) name servers13

bull The minimum set size for Ve to cut Alice off from the zone will be the min-cut set Vcut = MinCut(vi VZ)13 bull This is the lower bound on Eversquos acquisition cost13

Verisign Public 13 16 13

 

     

 

   

Security Analysis Attack Cost13

bull Eve must own vantage points (Ve) and be able to use them 13 13Acquisition + usage costs13

bull Acquisition ca(Ve) can specific nodes even be purchased13 bull Core routers at ATampT may not be on sale like grandmarsquos PC is13 bull Eve may have to get her hands dirty (if shersquos able to)13

bull Usage cu(Ve t) nodes in Ve may cost per hour or may get reclaimed if detected13 bull If renting nodes then snooping is a function of rent13 bull If Eve acquires her own nodes operators may notice her13

13 13 13C(Ve t) = ca (Ve) + cu (Ve t)13

Verisign Public 13 17 13

 

 

 

       

Acquisition Cost The Cat and Mouse Game13

bull Alicersquos best defense is to make her CoT as large andtopologically diverse as possible13

bull Eve needs to know Alicersquos CoT (and all paths to VZrsquos name servers)13 bull Note knowing any AS path is an open challenge [1]13

bull We evaluate three example types of adversaries13 1 General does not know any path info13 2 Targeted knows Alicersquos path to VZ but not her CoTrsquos13 3 Nation State will try to compromise the largest ISPs first13

[1] Mao Z M Qiu L Wang J and Zhang Y 2005 On AS-level pathinference 2005 ACM SIGMETRICS13

Verisign Public 13 18 13

 

 

 

Eversquos Probability of Success13

bull General the probability that Eve can subvert Alicersquos min-cut set is (where n is the size of Ve)13

bull Targeted as Alice augments her min-cut set theprobability of compromise approaches the General case13

bull Nation State the adversary is not focused on Alicersquos CoT but Alicersquos chances are still augmented as sheincreases her min-cut set13

Verisign Public 13 19 13

 

   

 

 

   

  

 

Evaluation13

bull Simulated an AS-level topology using the Inet topology generator13 bull Simulate 22000 ASes13

bull Chose random ASes as VZ nodes and VCoT nodes13 bull Calculated min-cut set for VZ and VCoT combinations ranging

from 2-1113 bull Used shortest path routing metric to represent routing13

bull Also deployed actual Vantages CoT13 bull Vantages written in C++ with SQLite backed DB uses GPG to

verify witness communications13 bull httpwwwvantage-pointsorg13

bull Constantly automatically learns zones and polls13 bull Aligns costs with benefits verification aligns with needs13

Verisign Public 13 20 13

 

 

 

 

 

 

DNSSEC Crypto Key Learning + Verification13

bull First attempt to enhancecore Internet system with crypto13

bull DNSSEC zones create publicprivate keys13 bull Public key is DNSKEY13

bull Zones sign all RRsets and resolvers use DNSKEYs to verify them13 bull Each RRset has a signature attached to it RRSIG13

bull Resolvers are configured with a single root key and trust flows recursively down the hierarchy13

Verisign Public 13 9 13

Using a zonersquos key13 on a standard RRset 13 (the NS)13

Signature (RRSIG) will only verify with the13 DNSKEY if no

Data Signing Example13

data was modified13

Verisign Public 13 10 13

 

 

   

 

 

Getting the Keys13

bull Until a resolver gets DNSKEY(s) data can be spoofed13

bull Keys verified by secure delegations from parents to children13 bull So resolvers know DNSKEYs are not being spoofed13

bull DNSSECrsquos design needs the full hierarchy in orderto verify keys13 bull No middle ground either a key has a verifiable delegation or

you know nothing about it13 bull What if we just queried for crypto keys directly13

Verisign Public 13 11 13

 

 

 

   

       

Public Data = Distributed polling + structured observations13

bull Verify DNSKEYs through Communities of Trust (CoTs)13 bull Consistency and

redundancy become the verification metric13

bull The network topologically diverse vantages13 bull G = (V E)13 bull V = v0 v1 hellip vn13

bull Observations bind data to time and a network path13 bull Path σ(ij) = (vi hellip vj)13 bull Data (such as a DNSKEY) d13 bull Observation oi = (dj t σ(ij))13

Verisign Public 13 12 13

       

Public Data Model13 Path = σ13

i

Name Server Svj

Resolver vi

Public Data pd

KEY

Data di

KEY

Observation oi

Message mi

bull pdi = (ditk)13 bull Svj = pd0 pdm 13 bull m = (di SigK(di)) 13 bull hellip13

Verisign Public 13 13 13

   

   

Peer-to-Peer CoTs13

bull P2P CoTs Compartmentalize13 bull CoTs are manual13

bull Trust must be bootstrapped13 bull Observed data is signed by PGP key13

14 13Verisign Public 13

v

v

v

v

v

v

v

v v v

v

v

CoT 1

CoT 2 CoT 3

 

 

 

 

 

 

Threat Model for Key Learning Man in the Middle13

bull To attack Eve must see keys that are in transit 13 bull If she must own a

vantage ve in σ13 bull But she canrsquot

arbitrarily attack just anyone13 bull Attacks between a

resolver v and ai zonersquos name servers (VZ)13

bull Not a reduction of scope this is dictated by the nature of DNS13

bull Eve must expend a real cost to own these vantages13

Verisign Public 13 15 13

 

 

 

 

What Eve Really Needs to Do13

B

C

A

D

E

F

H

G

N

M

L I

J

K

bull To spoof Eve must be in the right place at the right time13 bull She must be able to

intercept responses from all (or most) name servers13

bull The minimum set size for Ve to cut Alice off from the zone will be the min-cut set Vcut = MinCut(vi VZ)13 bull This is the lower bound on Eversquos acquisition cost13

Verisign Public 13 16 13

 

     

 

   

Security Analysis Attack Cost13

bull Eve must own vantage points (Ve) and be able to use them 13 13Acquisition + usage costs13

bull Acquisition ca(Ve) can specific nodes even be purchased13 bull Core routers at ATampT may not be on sale like grandmarsquos PC is13 bull Eve may have to get her hands dirty (if shersquos able to)13

bull Usage cu(Ve t) nodes in Ve may cost per hour or may get reclaimed if detected13 bull If renting nodes then snooping is a function of rent13 bull If Eve acquires her own nodes operators may notice her13

13 13 13C(Ve t) = ca (Ve) + cu (Ve t)13

Verisign Public 13 17 13

 

 

 

       

Acquisition Cost The Cat and Mouse Game13

bull Alicersquos best defense is to make her CoT as large andtopologically diverse as possible13

bull Eve needs to know Alicersquos CoT (and all paths to VZrsquos name servers)13 bull Note knowing any AS path is an open challenge [1]13

bull We evaluate three example types of adversaries13 1 General does not know any path info13 2 Targeted knows Alicersquos path to VZ but not her CoTrsquos13 3 Nation State will try to compromise the largest ISPs first13

[1] Mao Z M Qiu L Wang J and Zhang Y 2005 On AS-level pathinference 2005 ACM SIGMETRICS13

Verisign Public 13 18 13

 

 

 

Eversquos Probability of Success13

bull General the probability that Eve can subvert Alicersquos min-cut set is (where n is the size of Ve)13

bull Targeted as Alice augments her min-cut set theprobability of compromise approaches the General case13

bull Nation State the adversary is not focused on Alicersquos CoT but Alicersquos chances are still augmented as sheincreases her min-cut set13

Verisign Public 13 19 13

 

   

 

 

   

  

 

Evaluation13

bull Simulated an AS-level topology using the Inet topology generator13 bull Simulate 22000 ASes13

bull Chose random ASes as VZ nodes and VCoT nodes13 bull Calculated min-cut set for VZ and VCoT combinations ranging

from 2-1113 bull Used shortest path routing metric to represent routing13

bull Also deployed actual Vantages CoT13 bull Vantages written in C++ with SQLite backed DB uses GPG to

verify witness communications13 bull httpwwwvantage-pointsorg13

bull Constantly automatically learns zones and polls13 bull Aligns costs with benefits verification aligns with needs13

Verisign Public 13 20 13

Using a zonersquos key13 on a standard RRset 13 (the NS)13

Signature (RRSIG) will only verify with the13 DNSKEY if no

Data Signing Example13

data was modified13

Verisign Public 13 10 13

 

 

   

 

 

Getting the Keys13

bull Until a resolver gets DNSKEY(s) data can be spoofed13

bull Keys verified by secure delegations from parents to children13 bull So resolvers know DNSKEYs are not being spoofed13

bull DNSSECrsquos design needs the full hierarchy in orderto verify keys13 bull No middle ground either a key has a verifiable delegation or

you know nothing about it13 bull What if we just queried for crypto keys directly13

Verisign Public 13 11 13

 

 

 

   

       

Public Data = Distributed polling + structured observations13

bull Verify DNSKEYs through Communities of Trust (CoTs)13 bull Consistency and

redundancy become the verification metric13

bull The network topologically diverse vantages13 bull G = (V E)13 bull V = v0 v1 hellip vn13

bull Observations bind data to time and a network path13 bull Path σ(ij) = (vi hellip vj)13 bull Data (such as a DNSKEY) d13 bull Observation oi = (dj t σ(ij))13

Verisign Public 13 12 13

       

Public Data Model13 Path = σ13

i

Name Server Svj

Resolver vi

Public Data pd

KEY

Data di

KEY

Observation oi

Message mi

bull pdi = (ditk)13 bull Svj = pd0 pdm 13 bull m = (di SigK(di)) 13 bull hellip13

Verisign Public 13 13 13

   

   

Peer-to-Peer CoTs13

bull P2P CoTs Compartmentalize13 bull CoTs are manual13

bull Trust must be bootstrapped13 bull Observed data is signed by PGP key13

14 13Verisign Public 13

v

v

v

v

v

v

v

v v v

v

v

CoT 1

CoT 2 CoT 3

 

 

 

 

 

 

Threat Model for Key Learning Man in the Middle13

bull To attack Eve must see keys that are in transit 13 bull If she must own a

vantage ve in σ13 bull But she canrsquot

arbitrarily attack just anyone13 bull Attacks between a

resolver v and ai zonersquos name servers (VZ)13

bull Not a reduction of scope this is dictated by the nature of DNS13

bull Eve must expend a real cost to own these vantages13

Verisign Public 13 15 13

 

 

 

 

What Eve Really Needs to Do13

B

C

A

D

E

F

H

G

N

M

L I

J

K

bull To spoof Eve must be in the right place at the right time13 bull She must be able to

intercept responses from all (or most) name servers13

bull The minimum set size for Ve to cut Alice off from the zone will be the min-cut set Vcut = MinCut(vi VZ)13 bull This is the lower bound on Eversquos acquisition cost13

Verisign Public 13 16 13

 

     

 

   

Security Analysis Attack Cost13

bull Eve must own vantage points (Ve) and be able to use them 13 13Acquisition + usage costs13

bull Acquisition ca(Ve) can specific nodes even be purchased13 bull Core routers at ATampT may not be on sale like grandmarsquos PC is13 bull Eve may have to get her hands dirty (if shersquos able to)13

bull Usage cu(Ve t) nodes in Ve may cost per hour or may get reclaimed if detected13 bull If renting nodes then snooping is a function of rent13 bull If Eve acquires her own nodes operators may notice her13

13 13 13C(Ve t) = ca (Ve) + cu (Ve t)13

Verisign Public 13 17 13

 

 

 

       

Acquisition Cost The Cat and Mouse Game13

bull Alicersquos best defense is to make her CoT as large andtopologically diverse as possible13

bull Eve needs to know Alicersquos CoT (and all paths to VZrsquos name servers)13 bull Note knowing any AS path is an open challenge [1]13

bull We evaluate three example types of adversaries13 1 General does not know any path info13 2 Targeted knows Alicersquos path to VZ but not her CoTrsquos13 3 Nation State will try to compromise the largest ISPs first13

[1] Mao Z M Qiu L Wang J and Zhang Y 2005 On AS-level pathinference 2005 ACM SIGMETRICS13

Verisign Public 13 18 13

 

 

 

Eversquos Probability of Success13

bull General the probability that Eve can subvert Alicersquos min-cut set is (where n is the size of Ve)13

bull Targeted as Alice augments her min-cut set theprobability of compromise approaches the General case13

bull Nation State the adversary is not focused on Alicersquos CoT but Alicersquos chances are still augmented as sheincreases her min-cut set13

Verisign Public 13 19 13

 

   

 

 

   

  

 

Evaluation13

bull Simulated an AS-level topology using the Inet topology generator13 bull Simulate 22000 ASes13

bull Chose random ASes as VZ nodes and VCoT nodes13 bull Calculated min-cut set for VZ and VCoT combinations ranging

from 2-1113 bull Used shortest path routing metric to represent routing13

bull Also deployed actual Vantages CoT13 bull Vantages written in C++ with SQLite backed DB uses GPG to

verify witness communications13 bull httpwwwvantage-pointsorg13

bull Constantly automatically learns zones and polls13 bull Aligns costs with benefits verification aligns with needs13

Verisign Public 13 20 13

 

 

   

 

 

Getting the Keys13

bull Until a resolver gets DNSKEY(s) data can be spoofed13

bull Keys verified by secure delegations from parents to children13 bull So resolvers know DNSKEYs are not being spoofed13

bull DNSSECrsquos design needs the full hierarchy in orderto verify keys13 bull No middle ground either a key has a verifiable delegation or

you know nothing about it13 bull What if we just queried for crypto keys directly13

Verisign Public 13 11 13

 

 

 

   

       

Public Data = Distributed polling + structured observations13

bull Verify DNSKEYs through Communities of Trust (CoTs)13 bull Consistency and

redundancy become the verification metric13

bull The network topologically diverse vantages13 bull G = (V E)13 bull V = v0 v1 hellip vn13

bull Observations bind data to time and a network path13 bull Path σ(ij) = (vi hellip vj)13 bull Data (such as a DNSKEY) d13 bull Observation oi = (dj t σ(ij))13

Verisign Public 13 12 13

       

Public Data Model13 Path = σ13

i

Name Server Svj

Resolver vi

Public Data pd

KEY

Data di

KEY

Observation oi

Message mi

bull pdi = (ditk)13 bull Svj = pd0 pdm 13 bull m = (di SigK(di)) 13 bull hellip13

Verisign Public 13 13 13

   

   

Peer-to-Peer CoTs13

bull P2P CoTs Compartmentalize13 bull CoTs are manual13

bull Trust must be bootstrapped13 bull Observed data is signed by PGP key13

14 13Verisign Public 13

v

v

v

v

v

v

v

v v v

v

v

CoT 1

CoT 2 CoT 3

 

 

 

 

 

 

Threat Model for Key Learning Man in the Middle13

bull To attack Eve must see keys that are in transit 13 bull If she must own a

vantage ve in σ13 bull But she canrsquot

arbitrarily attack just anyone13 bull Attacks between a

resolver v and ai zonersquos name servers (VZ)13

bull Not a reduction of scope this is dictated by the nature of DNS13

bull Eve must expend a real cost to own these vantages13

Verisign Public 13 15 13

 

 

 

 

What Eve Really Needs to Do13

B

C

A

D

E

F

H

G

N

M

L I

J

K

bull To spoof Eve must be in the right place at the right time13 bull She must be able to

intercept responses from all (or most) name servers13

bull The minimum set size for Ve to cut Alice off from the zone will be the min-cut set Vcut = MinCut(vi VZ)13 bull This is the lower bound on Eversquos acquisition cost13

Verisign Public 13 16 13

 

     

 

   

Security Analysis Attack Cost13

bull Eve must own vantage points (Ve) and be able to use them 13 13Acquisition + usage costs13

bull Acquisition ca(Ve) can specific nodes even be purchased13 bull Core routers at ATampT may not be on sale like grandmarsquos PC is13 bull Eve may have to get her hands dirty (if shersquos able to)13

bull Usage cu(Ve t) nodes in Ve may cost per hour or may get reclaimed if detected13 bull If renting nodes then snooping is a function of rent13 bull If Eve acquires her own nodes operators may notice her13

13 13 13C(Ve t) = ca (Ve) + cu (Ve t)13

Verisign Public 13 17 13

 

 

 

       

Acquisition Cost The Cat and Mouse Game13

bull Alicersquos best defense is to make her CoT as large andtopologically diverse as possible13

bull Eve needs to know Alicersquos CoT (and all paths to VZrsquos name servers)13 bull Note knowing any AS path is an open challenge [1]13

bull We evaluate three example types of adversaries13 1 General does not know any path info13 2 Targeted knows Alicersquos path to VZ but not her CoTrsquos13 3 Nation State will try to compromise the largest ISPs first13

[1] Mao Z M Qiu L Wang J and Zhang Y 2005 On AS-level pathinference 2005 ACM SIGMETRICS13

Verisign Public 13 18 13

 

 

 

Eversquos Probability of Success13

bull General the probability that Eve can subvert Alicersquos min-cut set is (where n is the size of Ve)13

bull Targeted as Alice augments her min-cut set theprobability of compromise approaches the General case13

bull Nation State the adversary is not focused on Alicersquos CoT but Alicersquos chances are still augmented as sheincreases her min-cut set13

Verisign Public 13 19 13

 

   

 

 

   

  

 

Evaluation13

bull Simulated an AS-level topology using the Inet topology generator13 bull Simulate 22000 ASes13

bull Chose random ASes as VZ nodes and VCoT nodes13 bull Calculated min-cut set for VZ and VCoT combinations ranging

from 2-1113 bull Used shortest path routing metric to represent routing13

bull Also deployed actual Vantages CoT13 bull Vantages written in C++ with SQLite backed DB uses GPG to

verify witness communications13 bull httpwwwvantage-pointsorg13

bull Constantly automatically learns zones and polls13 bull Aligns costs with benefits verification aligns with needs13

Verisign Public 13 20 13

 

 

 

   

       

Public Data = Distributed polling + structured observations13

bull Verify DNSKEYs through Communities of Trust (CoTs)13 bull Consistency and

redundancy become the verification metric13

bull The network topologically diverse vantages13 bull G = (V E)13 bull V = v0 v1 hellip vn13

bull Observations bind data to time and a network path13 bull Path σ(ij) = (vi hellip vj)13 bull Data (such as a DNSKEY) d13 bull Observation oi = (dj t σ(ij))13

Verisign Public 13 12 13

       

Public Data Model13 Path = σ13

i

Name Server Svj

Resolver vi

Public Data pd

KEY

Data di

KEY

Observation oi

Message mi

bull pdi = (ditk)13 bull Svj = pd0 pdm 13 bull m = (di SigK(di)) 13 bull hellip13

Verisign Public 13 13 13

   

   

Peer-to-Peer CoTs13

bull P2P CoTs Compartmentalize13 bull CoTs are manual13

bull Trust must be bootstrapped13 bull Observed data is signed by PGP key13

14 13Verisign Public 13

v

v

v

v

v

v

v

v v v

v

v

CoT 1

CoT 2 CoT 3

 

 

 

 

 

 

Threat Model for Key Learning Man in the Middle13

bull To attack Eve must see keys that are in transit 13 bull If she must own a

vantage ve in σ13 bull But she canrsquot

arbitrarily attack just anyone13 bull Attacks between a

resolver v and ai zonersquos name servers (VZ)13

bull Not a reduction of scope this is dictated by the nature of DNS13

bull Eve must expend a real cost to own these vantages13

Verisign Public 13 15 13

 

 

 

 

What Eve Really Needs to Do13

B

C

A

D

E

F

H

G

N

M

L I

J

K

bull To spoof Eve must be in the right place at the right time13 bull She must be able to

intercept responses from all (or most) name servers13

bull The minimum set size for Ve to cut Alice off from the zone will be the min-cut set Vcut = MinCut(vi VZ)13 bull This is the lower bound on Eversquos acquisition cost13

Verisign Public 13 16 13

 

     

 

   

Security Analysis Attack Cost13

bull Eve must own vantage points (Ve) and be able to use them 13 13Acquisition + usage costs13

bull Acquisition ca(Ve) can specific nodes even be purchased13 bull Core routers at ATampT may not be on sale like grandmarsquos PC is13 bull Eve may have to get her hands dirty (if shersquos able to)13

bull Usage cu(Ve t) nodes in Ve may cost per hour or may get reclaimed if detected13 bull If renting nodes then snooping is a function of rent13 bull If Eve acquires her own nodes operators may notice her13

13 13 13C(Ve t) = ca (Ve) + cu (Ve t)13

Verisign Public 13 17 13

 

 

 

       

Acquisition Cost The Cat and Mouse Game13

bull Alicersquos best defense is to make her CoT as large andtopologically diverse as possible13

bull Eve needs to know Alicersquos CoT (and all paths to VZrsquos name servers)13 bull Note knowing any AS path is an open challenge [1]13

bull We evaluate three example types of adversaries13 1 General does not know any path info13 2 Targeted knows Alicersquos path to VZ but not her CoTrsquos13 3 Nation State will try to compromise the largest ISPs first13

[1] Mao Z M Qiu L Wang J and Zhang Y 2005 On AS-level pathinference 2005 ACM SIGMETRICS13

Verisign Public 13 18 13

 

 

 

Eversquos Probability of Success13

bull General the probability that Eve can subvert Alicersquos min-cut set is (where n is the size of Ve)13

bull Targeted as Alice augments her min-cut set theprobability of compromise approaches the General case13

bull Nation State the adversary is not focused on Alicersquos CoT but Alicersquos chances are still augmented as sheincreases her min-cut set13

Verisign Public 13 19 13

 

   

 

 

   

  

 

Evaluation13

bull Simulated an AS-level topology using the Inet topology generator13 bull Simulate 22000 ASes13

bull Chose random ASes as VZ nodes and VCoT nodes13 bull Calculated min-cut set for VZ and VCoT combinations ranging

from 2-1113 bull Used shortest path routing metric to represent routing13

bull Also deployed actual Vantages CoT13 bull Vantages written in C++ with SQLite backed DB uses GPG to

verify witness communications13 bull httpwwwvantage-pointsorg13

bull Constantly automatically learns zones and polls13 bull Aligns costs with benefits verification aligns with needs13

Verisign Public 13 20 13

       

Public Data Model13 Path = σ13

i

Name Server Svj

Resolver vi

Public Data pd

KEY

Data di

KEY

Observation oi

Message mi

bull pdi = (ditk)13 bull Svj = pd0 pdm 13 bull m = (di SigK(di)) 13 bull hellip13

Verisign Public 13 13 13

   

   

Peer-to-Peer CoTs13

bull P2P CoTs Compartmentalize13 bull CoTs are manual13

bull Trust must be bootstrapped13 bull Observed data is signed by PGP key13

14 13Verisign Public 13

v

v

v

v

v

v

v

v v v

v

v

CoT 1

CoT 2 CoT 3

 

 

 

 

 

 

Threat Model for Key Learning Man in the Middle13

bull To attack Eve must see keys that are in transit 13 bull If she must own a

vantage ve in σ13 bull But she canrsquot

arbitrarily attack just anyone13 bull Attacks between a

resolver v and ai zonersquos name servers (VZ)13

bull Not a reduction of scope this is dictated by the nature of DNS13

bull Eve must expend a real cost to own these vantages13

Verisign Public 13 15 13

 

 

 

 

What Eve Really Needs to Do13

B

C

A

D

E

F

H

G

N

M

L I

J

K

bull To spoof Eve must be in the right place at the right time13 bull She must be able to

intercept responses from all (or most) name servers13

bull The minimum set size for Ve to cut Alice off from the zone will be the min-cut set Vcut = MinCut(vi VZ)13 bull This is the lower bound on Eversquos acquisition cost13

Verisign Public 13 16 13

 

     

 

   

Security Analysis Attack Cost13

bull Eve must own vantage points (Ve) and be able to use them 13 13Acquisition + usage costs13

bull Acquisition ca(Ve) can specific nodes even be purchased13 bull Core routers at ATampT may not be on sale like grandmarsquos PC is13 bull Eve may have to get her hands dirty (if shersquos able to)13

bull Usage cu(Ve t) nodes in Ve may cost per hour or may get reclaimed if detected13 bull If renting nodes then snooping is a function of rent13 bull If Eve acquires her own nodes operators may notice her13

13 13 13C(Ve t) = ca (Ve) + cu (Ve t)13

Verisign Public 13 17 13

 

 

 

       

Acquisition Cost The Cat and Mouse Game13

bull Alicersquos best defense is to make her CoT as large andtopologically diverse as possible13

bull Eve needs to know Alicersquos CoT (and all paths to VZrsquos name servers)13 bull Note knowing any AS path is an open challenge [1]13

bull We evaluate three example types of adversaries13 1 General does not know any path info13 2 Targeted knows Alicersquos path to VZ but not her CoTrsquos13 3 Nation State will try to compromise the largest ISPs first13

[1] Mao Z M Qiu L Wang J and Zhang Y 2005 On AS-level pathinference 2005 ACM SIGMETRICS13

Verisign Public 13 18 13

 

 

 

Eversquos Probability of Success13

bull General the probability that Eve can subvert Alicersquos min-cut set is (where n is the size of Ve)13

bull Targeted as Alice augments her min-cut set theprobability of compromise approaches the General case13

bull Nation State the adversary is not focused on Alicersquos CoT but Alicersquos chances are still augmented as sheincreases her min-cut set13

Verisign Public 13 19 13

 

   

 

 

   

  

 

Evaluation13

bull Simulated an AS-level topology using the Inet topology generator13 bull Simulate 22000 ASes13

bull Chose random ASes as VZ nodes and VCoT nodes13 bull Calculated min-cut set for VZ and VCoT combinations ranging

from 2-1113 bull Used shortest path routing metric to represent routing13

bull Also deployed actual Vantages CoT13 bull Vantages written in C++ with SQLite backed DB uses GPG to

verify witness communications13 bull httpwwwvantage-pointsorg13

bull Constantly automatically learns zones and polls13 bull Aligns costs with benefits verification aligns with needs13

Verisign Public 13 20 13

   

   

Peer-to-Peer CoTs13

bull P2P CoTs Compartmentalize13 bull CoTs are manual13

bull Trust must be bootstrapped13 bull Observed data is signed by PGP key13

14 13Verisign Public 13

v

v

v

v

v

v

v

v v v

v

v

CoT 1

CoT 2 CoT 3

 

 

 

 

 

 

Threat Model for Key Learning Man in the Middle13

bull To attack Eve must see keys that are in transit 13 bull If she must own a

vantage ve in σ13 bull But she canrsquot

arbitrarily attack just anyone13 bull Attacks between a

resolver v and ai zonersquos name servers (VZ)13

bull Not a reduction of scope this is dictated by the nature of DNS13

bull Eve must expend a real cost to own these vantages13

Verisign Public 13 15 13

 

 

 

 

What Eve Really Needs to Do13

B

C

A

D

E

F

H

G

N

M

L I

J

K

bull To spoof Eve must be in the right place at the right time13 bull She must be able to

intercept responses from all (or most) name servers13

bull The minimum set size for Ve to cut Alice off from the zone will be the min-cut set Vcut = MinCut(vi VZ)13 bull This is the lower bound on Eversquos acquisition cost13

Verisign Public 13 16 13

 

     

 

   

Security Analysis Attack Cost13

bull Eve must own vantage points (Ve) and be able to use them 13 13Acquisition + usage costs13

bull Acquisition ca(Ve) can specific nodes even be purchased13 bull Core routers at ATampT may not be on sale like grandmarsquos PC is13 bull Eve may have to get her hands dirty (if shersquos able to)13

bull Usage cu(Ve t) nodes in Ve may cost per hour or may get reclaimed if detected13 bull If renting nodes then snooping is a function of rent13 bull If Eve acquires her own nodes operators may notice her13

13 13 13C(Ve t) = ca (Ve) + cu (Ve t)13

Verisign Public 13 17 13

 

 

 

       

Acquisition Cost The Cat and Mouse Game13

bull Alicersquos best defense is to make her CoT as large andtopologically diverse as possible13

bull Eve needs to know Alicersquos CoT (and all paths to VZrsquos name servers)13 bull Note knowing any AS path is an open challenge [1]13

bull We evaluate three example types of adversaries13 1 General does not know any path info13 2 Targeted knows Alicersquos path to VZ but not her CoTrsquos13 3 Nation State will try to compromise the largest ISPs first13

[1] Mao Z M Qiu L Wang J and Zhang Y 2005 On AS-level pathinference 2005 ACM SIGMETRICS13

Verisign Public 13 18 13

 

 

 

Eversquos Probability of Success13

bull General the probability that Eve can subvert Alicersquos min-cut set is (where n is the size of Ve)13

bull Targeted as Alice augments her min-cut set theprobability of compromise approaches the General case13

bull Nation State the adversary is not focused on Alicersquos CoT but Alicersquos chances are still augmented as sheincreases her min-cut set13

Verisign Public 13 19 13

 

   

 

 

   

  

 

Evaluation13

bull Simulated an AS-level topology using the Inet topology generator13 bull Simulate 22000 ASes13

bull Chose random ASes as VZ nodes and VCoT nodes13 bull Calculated min-cut set for VZ and VCoT combinations ranging

from 2-1113 bull Used shortest path routing metric to represent routing13

bull Also deployed actual Vantages CoT13 bull Vantages written in C++ with SQLite backed DB uses GPG to

verify witness communications13 bull httpwwwvantage-pointsorg13

bull Constantly automatically learns zones and polls13 bull Aligns costs with benefits verification aligns with needs13

Verisign Public 13 20 13

 

 

 

 

 

 

Threat Model for Key Learning Man in the Middle13

bull To attack Eve must see keys that are in transit 13 bull If she must own a

vantage ve in σ13 bull But she canrsquot

arbitrarily attack just anyone13 bull Attacks between a

resolver v and ai zonersquos name servers (VZ)13

bull Not a reduction of scope this is dictated by the nature of DNS13

bull Eve must expend a real cost to own these vantages13

Verisign Public 13 15 13

 

 

 

 

What Eve Really Needs to Do13

B

C

A

D

E

F

H

G

N

M

L I

J

K

bull To spoof Eve must be in the right place at the right time13 bull She must be able to

intercept responses from all (or most) name servers13

bull The minimum set size for Ve to cut Alice off from the zone will be the min-cut set Vcut = MinCut(vi VZ)13 bull This is the lower bound on Eversquos acquisition cost13

Verisign Public 13 16 13

 

     

 

   

Security Analysis Attack Cost13

bull Eve must own vantage points (Ve) and be able to use them 13 13Acquisition + usage costs13

bull Acquisition ca(Ve) can specific nodes even be purchased13 bull Core routers at ATampT may not be on sale like grandmarsquos PC is13 bull Eve may have to get her hands dirty (if shersquos able to)13

bull Usage cu(Ve t) nodes in Ve may cost per hour or may get reclaimed if detected13 bull If renting nodes then snooping is a function of rent13 bull If Eve acquires her own nodes operators may notice her13

13 13 13C(Ve t) = ca (Ve) + cu (Ve t)13

Verisign Public 13 17 13

 

 

 

       

Acquisition Cost The Cat and Mouse Game13

bull Alicersquos best defense is to make her CoT as large andtopologically diverse as possible13

bull Eve needs to know Alicersquos CoT (and all paths to VZrsquos name servers)13 bull Note knowing any AS path is an open challenge [1]13

bull We evaluate three example types of adversaries13 1 General does not know any path info13 2 Targeted knows Alicersquos path to VZ but not her CoTrsquos13 3 Nation State will try to compromise the largest ISPs first13

[1] Mao Z M Qiu L Wang J and Zhang Y 2005 On AS-level pathinference 2005 ACM SIGMETRICS13

Verisign Public 13 18 13

 

 

 

Eversquos Probability of Success13

bull General the probability that Eve can subvert Alicersquos min-cut set is (where n is the size of Ve)13

bull Targeted as Alice augments her min-cut set theprobability of compromise approaches the General case13

bull Nation State the adversary is not focused on Alicersquos CoT but Alicersquos chances are still augmented as sheincreases her min-cut set13

Verisign Public 13 19 13

 

   

 

 

   

  

 

Evaluation13

bull Simulated an AS-level topology using the Inet topology generator13 bull Simulate 22000 ASes13

bull Chose random ASes as VZ nodes and VCoT nodes13 bull Calculated min-cut set for VZ and VCoT combinations ranging

from 2-1113 bull Used shortest path routing metric to represent routing13

bull Also deployed actual Vantages CoT13 bull Vantages written in C++ with SQLite backed DB uses GPG to

verify witness communications13 bull httpwwwvantage-pointsorg13

bull Constantly automatically learns zones and polls13 bull Aligns costs with benefits verification aligns with needs13

Verisign Public 13 20 13

 

 

 

 

What Eve Really Needs to Do13

B

C

A

D

E

F

H

G

N

M

L I

J

K

bull To spoof Eve must be in the right place at the right time13 bull She must be able to

intercept responses from all (or most) name servers13

bull The minimum set size for Ve to cut Alice off from the zone will be the min-cut set Vcut = MinCut(vi VZ)13 bull This is the lower bound on Eversquos acquisition cost13

Verisign Public 13 16 13

 

     

 

   

Security Analysis Attack Cost13

bull Eve must own vantage points (Ve) and be able to use them 13 13Acquisition + usage costs13

bull Acquisition ca(Ve) can specific nodes even be purchased13 bull Core routers at ATampT may not be on sale like grandmarsquos PC is13 bull Eve may have to get her hands dirty (if shersquos able to)13

bull Usage cu(Ve t) nodes in Ve may cost per hour or may get reclaimed if detected13 bull If renting nodes then snooping is a function of rent13 bull If Eve acquires her own nodes operators may notice her13

13 13 13C(Ve t) = ca (Ve) + cu (Ve t)13

Verisign Public 13 17 13

 

 

 

       

Acquisition Cost The Cat and Mouse Game13

bull Alicersquos best defense is to make her CoT as large andtopologically diverse as possible13

bull Eve needs to know Alicersquos CoT (and all paths to VZrsquos name servers)13 bull Note knowing any AS path is an open challenge [1]13

bull We evaluate three example types of adversaries13 1 General does not know any path info13 2 Targeted knows Alicersquos path to VZ but not her CoTrsquos13 3 Nation State will try to compromise the largest ISPs first13

[1] Mao Z M Qiu L Wang J and Zhang Y 2005 On AS-level pathinference 2005 ACM SIGMETRICS13

Verisign Public 13 18 13

 

 

 

Eversquos Probability of Success13

bull General the probability that Eve can subvert Alicersquos min-cut set is (where n is the size of Ve)13

bull Targeted as Alice augments her min-cut set theprobability of compromise approaches the General case13

bull Nation State the adversary is not focused on Alicersquos CoT but Alicersquos chances are still augmented as sheincreases her min-cut set13

Verisign Public 13 19 13

 

   

 

 

   

  

 

Evaluation13

bull Simulated an AS-level topology using the Inet topology generator13 bull Simulate 22000 ASes13

bull Chose random ASes as VZ nodes and VCoT nodes13 bull Calculated min-cut set for VZ and VCoT combinations ranging

from 2-1113 bull Used shortest path routing metric to represent routing13

bull Also deployed actual Vantages CoT13 bull Vantages written in C++ with SQLite backed DB uses GPG to

verify witness communications13 bull httpwwwvantage-pointsorg13

bull Constantly automatically learns zones and polls13 bull Aligns costs with benefits verification aligns with needs13

Verisign Public 13 20 13

 

     

 

   

Security Analysis Attack Cost13

bull Eve must own vantage points (Ve) and be able to use them 13 13Acquisition + usage costs13

bull Acquisition ca(Ve) can specific nodes even be purchased13 bull Core routers at ATampT may not be on sale like grandmarsquos PC is13 bull Eve may have to get her hands dirty (if shersquos able to)13

bull Usage cu(Ve t) nodes in Ve may cost per hour or may get reclaimed if detected13 bull If renting nodes then snooping is a function of rent13 bull If Eve acquires her own nodes operators may notice her13

13 13 13C(Ve t) = ca (Ve) + cu (Ve t)13

Verisign Public 13 17 13

 

 

 

       

Acquisition Cost The Cat and Mouse Game13

bull Alicersquos best defense is to make her CoT as large andtopologically diverse as possible13

bull Eve needs to know Alicersquos CoT (and all paths to VZrsquos name servers)13 bull Note knowing any AS path is an open challenge [1]13

bull We evaluate three example types of adversaries13 1 General does not know any path info13 2 Targeted knows Alicersquos path to VZ but not her CoTrsquos13 3 Nation State will try to compromise the largest ISPs first13

[1] Mao Z M Qiu L Wang J and Zhang Y 2005 On AS-level pathinference 2005 ACM SIGMETRICS13

Verisign Public 13 18 13

 

 

 

Eversquos Probability of Success13

bull General the probability that Eve can subvert Alicersquos min-cut set is (where n is the size of Ve)13

bull Targeted as Alice augments her min-cut set theprobability of compromise approaches the General case13

bull Nation State the adversary is not focused on Alicersquos CoT but Alicersquos chances are still augmented as sheincreases her min-cut set13

Verisign Public 13 19 13

 

   

 

 

   

  

 

Evaluation13

bull Simulated an AS-level topology using the Inet topology generator13 bull Simulate 22000 ASes13

bull Chose random ASes as VZ nodes and VCoT nodes13 bull Calculated min-cut set for VZ and VCoT combinations ranging

from 2-1113 bull Used shortest path routing metric to represent routing13

bull Also deployed actual Vantages CoT13 bull Vantages written in C++ with SQLite backed DB uses GPG to

verify witness communications13 bull httpwwwvantage-pointsorg13

bull Constantly automatically learns zones and polls13 bull Aligns costs with benefits verification aligns with needs13

Verisign Public 13 20 13

 

 

 

       

Acquisition Cost The Cat and Mouse Game13

bull Alicersquos best defense is to make her CoT as large andtopologically diverse as possible13

bull Eve needs to know Alicersquos CoT (and all paths to VZrsquos name servers)13 bull Note knowing any AS path is an open challenge [1]13

bull We evaluate three example types of adversaries13 1 General does not know any path info13 2 Targeted knows Alicersquos path to VZ but not her CoTrsquos13 3 Nation State will try to compromise the largest ISPs first13

[1] Mao Z M Qiu L Wang J and Zhang Y 2005 On AS-level pathinference 2005 ACM SIGMETRICS13

Verisign Public 13 18 13

 

 

 

Eversquos Probability of Success13

bull General the probability that Eve can subvert Alicersquos min-cut set is (where n is the size of Ve)13

bull Targeted as Alice augments her min-cut set theprobability of compromise approaches the General case13

bull Nation State the adversary is not focused on Alicersquos CoT but Alicersquos chances are still augmented as sheincreases her min-cut set13

Verisign Public 13 19 13

 

   

 

 

   

  

 

Evaluation13

bull Simulated an AS-level topology using the Inet topology generator13 bull Simulate 22000 ASes13

bull Chose random ASes as VZ nodes and VCoT nodes13 bull Calculated min-cut set for VZ and VCoT combinations ranging

from 2-1113 bull Used shortest path routing metric to represent routing13

bull Also deployed actual Vantages CoT13 bull Vantages written in C++ with SQLite backed DB uses GPG to

verify witness communications13 bull httpwwwvantage-pointsorg13

bull Constantly automatically learns zones and polls13 bull Aligns costs with benefits verification aligns with needs13

Verisign Public 13 20 13

 

 

 

Eversquos Probability of Success13

bull General the probability that Eve can subvert Alicersquos min-cut set is (where n is the size of Ve)13

bull Targeted as Alice augments her min-cut set theprobability of compromise approaches the General case13

bull Nation State the adversary is not focused on Alicersquos CoT but Alicersquos chances are still augmented as sheincreases her min-cut set13

Verisign Public 13 19 13

 

   

 

 

   

  

 

Evaluation13

bull Simulated an AS-level topology using the Inet topology generator13 bull Simulate 22000 ASes13

bull Chose random ASes as VZ nodes and VCoT nodes13 bull Calculated min-cut set for VZ and VCoT combinations ranging

from 2-1113 bull Used shortest path routing metric to represent routing13

bull Also deployed actual Vantages CoT13 bull Vantages written in C++ with SQLite backed DB uses GPG to

verify witness communications13 bull httpwwwvantage-pointsorg13

bull Constantly automatically learns zones and polls13 bull Aligns costs with benefits verification aligns with needs13

Verisign Public 13 20 13

 

   

 

 

   

  

 

Evaluation13

bull Simulated an AS-level topology using the Inet topology generator13 bull Simulate 22000 ASes13

bull Chose random ASes as VZ nodes and VCoT nodes13 bull Calculated min-cut set for VZ and VCoT combinations ranging

from 2-1113 bull Used shortest path routing metric to represent routing13

bull Also deployed actual Vantages CoT13 bull Vantages written in C++ with SQLite backed DB uses GPG to

verify witness communications13 bull httpwwwvantage-pointsorg13

bull Constantly automatically learns zones and polls13 bull Aligns costs with benefits verification aligns with needs13

Verisign Public 13 20 13

 

 

 

Actual Measured Min Cut-Set Sizes13

bull Using a Vantages daemon peered with SecSpider we get the following actual min cut-set sizes for major DNS zones13 bull SecSpiderrsquos distributed key learning system online since 200613

21 13Verisign Public 13

bull These are on par with or better than our simulatedresults13

Actual Zone13 Min Cut-Set Size13 (root)13 2713 gov13 1813 br13 1813 bg13 1313 org13 1113 hellip13 hellip13

     

   

   

Simulated General Adversary13

bull Ran 10X10 simulations13 bull CoTs = [1-10]13 bull VZ = [2-11]13

bull General Adversary13 bull 90 ASes = 1013

13

bull Nation State13 bull 89 ASes = 2013

Verisign Public 13 22 13

 

   

 

   

 

Conclusions13

bull With Public Data we seek to add an orthogonalsubstrate to our systems feasibility tested withVantages13 bull Large TLD failures did not black out Vantagesrsquo view of the tree13 bull When the rootrsquos DURZ unblinded Vantages automatically

bootstrapped and learned it13

bull Fixing these problems in DNSSEC allows systems built on DNSSEC to inherit robustness13 bull DNSSEC must be robust to misconfigs and outages13 bull People are adding services on DNS (DANE and more)13

bull Our Vantages deployment suggests its assurances areon par (or even better than) our simulated results13

Verisign Public 13 23 13

Thank You13

Check out our technical report 13 httptechreportsverisignlabscomtr-lookupcgitrid=1110001amprev=1 13

copy 2012 VeriSign Inc All rights reserved VERISIGN and other trademarks service marks anddesigns are registered or unregistered trademarks of VeriSign Inc and its subsidiaries in the UnitedStates and in foreign countries All other trademarks are property of their respective owners13

     

   

   

Simulated General Adversary13

bull Ran 10X10 simulations13 bull CoTs = [1-10]13 bull VZ = [2-11]13

bull General Adversary13 bull 90 ASes = 1013

13

bull Nation State13 bull 89 ASes = 2013

Verisign Public 13 22 13

 

   

 

   

 

Conclusions13

bull With Public Data we seek to add an orthogonalsubstrate to our systems feasibility tested withVantages13 bull Large TLD failures did not black out Vantagesrsquo view of the tree13 bull When the rootrsquos DURZ unblinded Vantages automatically

bootstrapped and learned it13

bull Fixing these problems in DNSSEC allows systems built on DNSSEC to inherit robustness13 bull DNSSEC must be robust to misconfigs and outages13 bull People are adding services on DNS (DANE and more)13

bull Our Vantages deployment suggests its assurances areon par (or even better than) our simulated results13

Verisign Public 13 23 13

Thank You13

Check out our technical report 13 httptechreportsverisignlabscomtr-lookupcgitrid=1110001amprev=1 13

copy 2012 VeriSign Inc All rights reserved VERISIGN and other trademarks service marks anddesigns are registered or unregistered trademarks of VeriSign Inc and its subsidiaries in the UnitedStates and in foreign countries All other trademarks are property of their respective owners13

 

   

 

   

 

Conclusions13

bull With Public Data we seek to add an orthogonalsubstrate to our systems feasibility tested withVantages13 bull Large TLD failures did not black out Vantagesrsquo view of the tree13 bull When the rootrsquos DURZ unblinded Vantages automatically

bootstrapped and learned it13

bull Fixing these problems in DNSSEC allows systems built on DNSSEC to inherit robustness13 bull DNSSEC must be robust to misconfigs and outages13 bull People are adding services on DNS (DANE and more)13

bull Our Vantages deployment suggests its assurances areon par (or even better than) our simulated results13

Verisign Public 13 23 13

Thank You13

Check out our technical report 13 httptechreportsverisignlabscomtr-lookupcgitrid=1110001amprev=1 13

copy 2012 VeriSign Inc All rights reserved VERISIGN and other trademarks service marks anddesigns are registered or unregistered trademarks of VeriSign Inc and its subsidiaries in the UnitedStates and in foreign countries All other trademarks are property of their respective owners13

Thank You13

Check out our technical report 13 httptechreportsverisignlabscomtr-lookupcgitrid=1110001amprev=1 13

copy 2012 VeriSign Inc All rights reserved VERISIGN and other trademarks service marks anddesigns are registered or unregistered trademarks of VeriSign Inc and its subsidiaries in the UnitedStates and in foreign countries All other trademarks are property of their respective owners13

Backup13

Verisign Public 13 25 13

 

 

 

 

General Lessons from Deployment Problems13

bull ldquoDistributing the authority for a [crypto-enhancedsystem] does not distribute the corresponding amountof expertiserdquo13

-- Paul Mockapetris13

bull Simple designs do not always equate to simpleoperations13

bull Cryptography adds a lot of operational complexity13

bull Failing to consider operational realities can result inserious outages13

Verisign Public 13 26 13

 

     

 

 

     

Public Data Key Learning and Verification13

bull Motivated by measurements of the hierarchal model13

bull Goal get proper keys for zones to resolvers13 bull Avoid being spoofed without the hierarchy13 bull Use redundancy for protection13

bull Verification is now a measurable property of publically available data13 bull The more independent measurements the more secure13

bull Community of Trust (CoT) Trust is subjective13 bull Cross-check what you see with what your friends saw13 bull This is not the Web of Trust observations not attestations13

Verisign Public 13 27 13

Public Data Model (again)13

Verisign Public 13 28 13

Public Data Model (again)13

Verisign Public 13 29 13

General Adversary13

Verisign Public 13 30 13

Targeted Adversary13

Verisign Public 13 31 13

Nation State Adversary13

Verisign Public 13 32 13

 

   

 

 

Vantages Implementation13

bull Written in C++ with SQLite backed DB uses GPG toverify witness communications13 bull Installs and can start running right away13 bull httpwwwvantage-pointsorg13

bull Can be administered via web admin interface13

bull Automatically learns zones and polls every day13

Verisign Public 13 33 13

 

 

 

 

General Lessons from Deployment Problems13

bull ldquoDistributing the authority for a [crypto-enhancedsystem] does not distribute the corresponding amountof expertiserdquo13

-- Paul Mockapetris13

bull Simple designs do not always equate to simpleoperations13

bull Cryptography adds a lot of operational complexity13

bull Failing to consider operational realities can result inserious outages13

Verisign Public 13 26 13

 

     

 

 

     

Public Data Key Learning and Verification13

bull Motivated by measurements of the hierarchal model13

bull Goal get proper keys for zones to resolvers13 bull Avoid being spoofed without the hierarchy13 bull Use redundancy for protection13

bull Verification is now a measurable property of publically available data13 bull The more independent measurements the more secure13

bull Community of Trust (CoT) Trust is subjective13 bull Cross-check what you see with what your friends saw13 bull This is not the Web of Trust observations not attestations13

Verisign Public 13 27 13

Public Data Model (again)13

Verisign Public 13 28 13

Public Data Model (again)13

Verisign Public 13 29 13

General Adversary13

Verisign Public 13 30 13

Targeted Adversary13

Verisign Public 13 31 13

Nation State Adversary13

Verisign Public 13 32 13

 

   

 

 

Vantages Implementation13

bull Written in C++ with SQLite backed DB uses GPG toverify witness communications13 bull Installs and can start running right away13 bull httpwwwvantage-pointsorg13

bull Can be administered via web admin interface13

bull Automatically learns zones and polls every day13

Verisign Public 13 33 13

 

     

 

 

     

Public Data Key Learning and Verification13

bull Motivated by measurements of the hierarchal model13

bull Goal get proper keys for zones to resolvers13 bull Avoid being spoofed without the hierarchy13 bull Use redundancy for protection13

bull Verification is now a measurable property of publically available data13 bull The more independent measurements the more secure13

bull Community of Trust (CoT) Trust is subjective13 bull Cross-check what you see with what your friends saw13 bull This is not the Web of Trust observations not attestations13

Verisign Public 13 27 13

Public Data Model (again)13

Verisign Public 13 28 13

Public Data Model (again)13

Verisign Public 13 29 13

General Adversary13

Verisign Public 13 30 13

Targeted Adversary13

Verisign Public 13 31 13

Nation State Adversary13

Verisign Public 13 32 13

 

   

 

 

Vantages Implementation13

bull Written in C++ with SQLite backed DB uses GPG toverify witness communications13 bull Installs and can start running right away13 bull httpwwwvantage-pointsorg13

bull Can be administered via web admin interface13

bull Automatically learns zones and polls every day13

Verisign Public 13 33 13

Public Data Model (again)13

Verisign Public 13 28 13

Public Data Model (again)13

Verisign Public 13 29 13

General Adversary13

Verisign Public 13 30 13

Targeted Adversary13

Verisign Public 13 31 13

Nation State Adversary13

Verisign Public 13 32 13

 

   

 

 

Vantages Implementation13

bull Written in C++ with SQLite backed DB uses GPG toverify witness communications13 bull Installs and can start running right away13 bull httpwwwvantage-pointsorg13

bull Can be administered via web admin interface13

bull Automatically learns zones and polls every day13

Verisign Public 13 33 13

Public Data Model (again)13

Verisign Public 13 29 13

General Adversary13

Verisign Public 13 30 13

Targeted Adversary13

Verisign Public 13 31 13

Nation State Adversary13

Verisign Public 13 32 13

 

   

 

 

Vantages Implementation13

bull Written in C++ with SQLite backed DB uses GPG toverify witness communications13 bull Installs and can start running right away13 bull httpwwwvantage-pointsorg13

bull Can be administered via web admin interface13

bull Automatically learns zones and polls every day13

Verisign Public 13 33 13

General Adversary13

Verisign Public 13 30 13

Targeted Adversary13

Verisign Public 13 31 13

Nation State Adversary13

Verisign Public 13 32 13

 

   

 

 

Vantages Implementation13

bull Written in C++ with SQLite backed DB uses GPG toverify witness communications13 bull Installs and can start running right away13 bull httpwwwvantage-pointsorg13

bull Can be administered via web admin interface13

bull Automatically learns zones and polls every day13

Verisign Public 13 33 13

Targeted Adversary13

Verisign Public 13 31 13

Nation State Adversary13

Verisign Public 13 32 13

 

   

 

 

Vantages Implementation13

bull Written in C++ with SQLite backed DB uses GPG toverify witness communications13 bull Installs and can start running right away13 bull httpwwwvantage-pointsorg13

bull Can be administered via web admin interface13

bull Automatically learns zones and polls every day13

Verisign Public 13 33 13

Nation State Adversary13

Verisign Public 13 32 13

 

   

 

 

Vantages Implementation13

bull Written in C++ with SQLite backed DB uses GPG toverify witness communications13 bull Installs and can start running right away13 bull httpwwwvantage-pointsorg13

bull Can be administered via web admin interface13

bull Automatically learns zones and polls every day13

Verisign Public 13 33 13

 

   

 

 

Vantages Implementation13

bull Written in C++ with SQLite backed DB uses GPG toverify witness communications13 bull Installs and can start running right away13 bull httpwwwvantage-pointsorg13

bull Can be administered via web admin interface13

bull Automatically learns zones and polls every day13

Verisign Public 13 33 13

 

   

   

 

Peer-to-Peer CoTs13

bull Vantage daemons learnWeb DNSKEYs from DNS or web Scraper

pages13 bull Cross-check within CoT13 bull P2P CoTs

Compartmentalize13 bull CoTs are manual13

bull Trust must be bootstrapped13 bull Observed data is signed by

GPG key13

Verisign Public 13 34 13

dnskeyorg

Raw Data

Processed Data

DNS Scraper

dnskeyorg

ConsistencyScraper

dnskeyorg

lthtmlgt

lthtmlgt

httpdnskeyorg

dnssecdnskeyorg

v

v

v

v

v

v

v

v v v

v

v

CoT 1

CoT 2 CoT 3

 

 

     

Vantages A Public Data System13

bull Real system implementing Public Data needs somepractical re-mappings13 bull Some nodes may offer a set of observations (such as

SecSpider) cull data from different protocols etc13

bull Everyone runs their own Vantage daemon13 bull Peer-to-peer choose your own CoT13 bull Avoids the ldquowhorsquos going to run itrdquo question13

Verisign Public 13 35 13

 

 

     

Vantages A Public Data System13

bull Real system implementing Public Data needs somepractical re-mappings13 bull Some nodes may offer a set of observations (such as

SecSpider) cull data from different protocols etc13

bull Everyone runs their own Vantage daemon13 bull Peer-to-peer choose your own CoT13 bull Avoids the ldquowhorsquos going to run itrdquo question13

Verisign Public 13 35 13