Detection of Interactive Stepping Stones Shobha Venkataraman shobha@cs.cmu.edu Joint work with Avrim...

Detection of Interactive Stepping Stones

Shobha Venkataramanshobha@cs.cmu.edu

Joint work with Avrim Blum & Dawn Song

Carnegie Mellon University

ICML Workshop 2006

June 29, 2006

Stepping Stone

stepping-stone attack: attacker uses chain of compromised machines to reach victim

Difficult to find attacker from looking only at victim Victim only sees the last host in chain

Attacker

A

X1

Victim

V

Xk

Why stepping-stones? Stepping-stones attractive to attackers

Ease of compromising hosts on Internet Difficulty of detection

Don’t know when host is compromised Only know when there is attack

Don’t know who compromised Chaos and volume of Internet traffic Not always logged

True attacker almost untraceable: near-perfect way to achieve anonymity!

Large-scale stepping-stones: botnets…

Botnets: “For sale, stepping-stones” Botnets: Set of compromised hosts controlled by a

single “command-center” How this works:

individual hosts compromised “control priveleges” sold to other attackers, who use them launch

attacks. Nearly impossible to discover true attackers Extremely prevalent on Internet

Logs at CMU dept: discovered Gaobot infection Across 6-7 months of traffic (everything we examined!) Across 100+ hosts (1/10th network) at peak infection

Botnets (II)CMU

Stanford

PittsburghVerizon DSL

VICTIM

Attacker #1

Attacker #2

General Stepping-Stone Detection

Extremely difficult: Indefinite delay between stepping-stone “legs” Traffic too voluminous/insufficiently logged for

traceback Packets encrypted or padded between “legs” Stepping-stone “legs” additionally masked by

adding superfluous traffic (“chaff”)

Restricting the Problem Restrictions:

Traffic monitoring done at routers/gateways Interactive stepping-stone streams

Bounded delay between stepping-stones

A

X1 X2

VS2 S1

M1M2

InternetT = 0 T

Restricting the Problem (II)

Restrictions put together: observe 2 time-delayed streams at monitor, are they a stepping-stone pair?

If attacker uses no chaff If attacker uses chaff

A

X1 X2

VS2 S1

InternetT = 0 T

Prior work Donoho, Flesia, Shankar, Paxson, Coit & Staniford, RAID 02

Assumptions needed: Attack stream from Poisson or Pareto distribution Normal users perfectly uncorrelated

No guarantees on monitoring time or false positives Wang & Reeves, CCS 03

Assumptions needed: Timing perturbation of packets iid [strong assumption] No chaff

Scheme breaks without assumptions

Other related work: [SH95, YE00, ZP00, WRWY01, WRW02, W04]

Our work Want to allow correlations among normal users

Don’t flag just any correlated pair Time-correlated pair != stepping-stone pair

Use milder assumptions Model non-attack streams as sequences of Poisson

processes No additional assumption on attacker Allow chaff

Present algorithms and analysis for these models`

Inspiration from learning theory

Learning Theory Question:

How many examples do we need to see before we can identify hypotheses with guaranteed confidence?

Our Question:

How many packets do we need to see before we can identify normal/attack streams with guaranteed confidence?

Rest of talk: answer this question…

Outline

Problem definition Without chaff

Simple Poisson model Generalized Poisson model

With chaff Algorithms Hardness of detection results

Conclusions

Problem Definition (I) Set-up: stepping-stone monitor tracks no. of packets in

streams S1, S2 at a given time t : N1(t), N2(t) Assumptions:

Packets correspond 1-1 on stepping-stone streams(without

chaff)

Max tolerable delay bound exists Max no. packets attacker can send in time exists: p

Our bounds will be in terms of p.

Problem Definition (II) For stepping-stone streams S1 & S2 :

1. Every packet on S2 comes from S1

N1(t) N2(t)

2. Every packet on S1 appears on S2 within time

N1(t) N2(t + ) Assumptions on normal streams next… Detect stepping-stone pairs with guarantees on:

Monitoring time Mtotal packets observed on both streams before detection

False-positive probability

Simple Poisson Model Assumptions:

Normal stream: Poisson process with fixed rates (generalize this later)

p is known (relax this later). No chaff (generalize this later).

Outline: Algorithm Analysis sketch Relax knowledge of p

Algorithm

Algorithm Observe y packets on union of streams S1 and S2

Compute difference in no. of packets d = N1 – N2. If d is not in [- p, p, return NORMAL Repeat over x iterations the above procedure Return ATTACK if d lies in [-p, p throughout

Thm: with x = log 1/, y = 2(p2 Monitoring time M = xy = O(p

2 log 1 packets False positives <

S2S1

Analysis (I) Overhead:

Only per-stream packet counters running all the time! Compute sums & differences for pairs once in a while Algorithm needs NO knowledge of Poisson rates

Any stepping-stone pair sending M packets reported

For stepping-stone pair, d within [-p, p] If |d| > p,some packet violates max delay bound

Ensure that false positive probability less than i.e. d leaves [-p, p] with probability more than 1 - When d leaves [-p, p], algorithm returns “normal”

Analysis (II) Streams S1 and S2 Poisson processes with rates 1, 2

(normalized so that 1 + 2 = 1) On union of streams, each packet:

1 chance of coming from S1, 2 chance of coming from S2

Stream 1 Stream 2

Time

Analysis (III)

Every time packet appears on S1 S2

Z = Z + 1 with probability 1

Z = Z - 1 with probability 2

Thus, Z equivalent to 1-d random walk Need Z to exit [-p, p] after some steps

10-1-2 2

2

1 1

22 2Z 1 0 1 2 1 0 1 0 0 1 01

Let Z be the difference in no. of packets on S1 and S2

Stream 1 Stream 2

Analysis (IV)

Fact: 1-d random walk exits bounded region of length t in expected O(t2) time! Therefore,

When n = O(p2) ,

Pr[Z will stay in bounded region] < 1/2 Repeat for m = log 1/ iterations

Pr[Z will stay in bounded region] < When Z exits bounded region, normal pair does not get falsely accused. Done!

2

1 1

22 2

10-1-2 2

What if p is unknown?

What if we do not know p? Use “guess and double” strategy.

Set pj = 2j.

Run algorithm over sequence of pj: p1, p2, …

When a pair is “cleared” for pj, examine it with respect to pj+1..

What if p is unknown? For stepping-stone pair, increases monitoring time by

O(log log p). Guarantee depends only on true value of p!

In practice, set upper bound for p Normal streams monitored until upper bound reached

As j increases, test differences exponentially less often Fundamental problem: cannot distinguish between normal pair

and attack pair with longer delay bound

Summary: Simple Poisson Normal streams: Poisson process with single

fixed rate. Algorithm with guaranteed false positives and

monitoring time Algorithm needs no knowledge of Poisson rates

Analysis extended When p is unknown When false positive probability is distributed over all

pairs of streams: in paper

Outline Problem definition Without chaff

Simple Poisson model Generalized Poisson model

With chaff Algorithms Hardness of detection results

Conclusions

Generalized Poisson model Model normal process as SEQUENCE of Poisson

processes: varying rates for varying time periods

i.e. stream given by: (1, t1), (2, t2), … General model: coarsely approximate almost any usage

pattern, for example: Coarsely simulate Pareto distributions – good model of typing

patterns Correlated users: same sequence of Poisson rates &time

intervals

Analysis Sketch Formally, a stream S is given by: (1, t1), (2, t2), … Key observation:

At time T, packet distribution equivalent to Poisson process with single fixed rate j (j . tj)/T (weighted mean)

More details in paper.

2

1 1

22 2

10-1-2 2

Summary: General Poisson Normal streams modelled as sequences of Poisson

processes: (1, t1), (2, t2), …

Very general model Algorithm with guarantees on monitoring time and false

positive rate Once again, algorithm needs no knowledge of Poisson rates

Results in this model extended similarly: When p is unknown When false positive probability is distributed over all pairs of

streams

Outline Problem definition Without chaff

Simple Poisson model Generalized Poisson model

With chaff Algorithms Hardness of detection results

Conclusions

Chaff

Algorithms (as presented) broken by single packet of chaff Next, modify algorithms to handle limited chaff…

Attacker Victim

Stepping Stone

Chaff: dummy packets inserted in traffic streams to avoid detection

Chaff: Algorithms Fix chaff rate, but chaff arbitrarily distributed Simple Poisson model

Algorithm: Let y be number of packets needed before we exit bounded

region in random walk. Allow chaff rate of p/4y, monitor for difference to leave [-2p, 2p] Regular streams get difference (wait longer)

Can tweak algorithm to handle slightly higher chaff rate, but that’s all. Hardness results next…

Extends similarly to general Poisson model.

Hardness of Detection No algorithm based on timing delays alone can detect

stepping-stones with smart use of chaff Can give bounds on chaff needed so attacker can

pre-generate two independent processes send packets to mimic independent processes exactly Details & strategies in paper

If attacker can actively send such chaff, detection requires use of other information

Summary Algorithms to detect stepping stones:

Guarantees on monitoring time and false positives Simple and generalized Poisson models With and without (arbitrarily distributed) chaff When p is known/unknown

Compared to previous work: Milder assumptions, allow for substantial correlation among

normal users No additional assumptions on attacker (besides delay bound)

With sufficient chaff, attacker can mask stepping stones, so that no algorithm that uses inter-packet delays can detect them.

Prior Work