Defending Sybil Attack in Peer2Peer Networks

Md. Tanvir Al Amin 04 09 05 2064Shah Md. Rifat Ahsan 10 09 05 2060

Adviser : Dr. Reaz Ahmed

Distributed Search Techniques

2

Sybil Attack A fundamental problem in

distributed systems.

Single user assumes many fake/sybil identities Already observed in real-world

p2p systems

Sybil identities can become a large fraction of all identities “Out-vote” honest users in

collaborative tasks

launchsybilattack

honest

malicious

Sybil attack

Present in both Application level and P2P Networking Attacker creates many fake/sybil identities Many cases of real world attacks : Digg, Youtube Several research works shown how easy it was to

subvert DHT like Chord or Kademlia using Sybil Attack

Automated sybil attack on Youtube for $147!Automated sybil attack on Youtube for $147!

Defending against Sybil attacks

Traditional solutions rely on central trusted authorities Runs counter to open membership policies of OSNs

Recent proposals leverage social networks Lots of research activity recently Each optimized under assumptions about the graph

structure Each evaluated on different datasets

SybilGuard [SIGCOMM’06]SybilLimit [Oakland’08]Ostra [NSDI’08]SumUp [NSDI’09]SybilInfer [NDSS’09]Whanau [NSDI’10]MobID [INFOCOM’10]

All schemes analyze the graph structure to isolate Sybils

Defending against Sybil attacks Recent proposals leverage social networks

Key Insight: Social links are hard to acquire in abundance Look for small cuts in the graph Conversely, look for communities around known trusted nodes Dunbar’s Number Power law node degrees

Links difficult to createLinks difficult to create

HOW DO SOCIAL NETWORKS LOOK LIKE

SybilGuard: Defending Against Sybil Attacksvia Social NetworksSybilguard is a system for detecting Sybil nodes in social graphs.

Features of Sybil Guard SybilGuard enables an honest node to identify other nodes Verifier node V can verify if suspect node S is malicious Guaranteed bound on number of sybil groups Guaranteed bound on size of sybil groups Completely decentralizeKey Insight: 1. Use a social network to limit Sybils

2.Social links are hard to acquire in abundance3.Look for small cuts in the graph

DBLP Network

Dunbar’s number Limits the # of stable social relationships a user can have

To less than a couple of hundred Linked to size of neo-cortex region of the brain Observed throughout history since hunter-gatherer societies

Roughly reported to be 150

Also observed repeatedly in studies of OSN user activity Users might have a large number of contacts But, regularly interact with less than a couple of hundred of them

Power-law node degrees

9U.S. highways U.S. Airlines

10

Path lengths and diameter all major networks have short path length from 4.25

– 5.88 six degrees of separation

Facebook, 4.2 million for Octorber 2007, 6.12 fromhttp://blog.paulwalk.net/2007/10/08/no-degrees-of-separation/

11

Implications of Path lengths and diameter

The small diameter and path lengths of social networks are likely to impact the design of techniques for finding paths in such networks

12

Link degree correlations high-degree nodes tend to connect to other high-degree nodes ? OR high-degree nodes tend to connect to low-degree nodes ? In real society: the former theory is true. By virtue of two metrics: the scale-free metric and the assortativity. Suggests that there exists a tightly-connected “core” of the

high-degree nodes which connect to each other, with the lower-degree nodes on the fringes of the network.

The next question: How big the core is

13

Implications of Link degree correlationsSpread of Information

“A Measurement-driven Analysis of Information Propagation in the Flickr Social Network” [WWW’ 09]

14

Densely connected core the graphs have a densely connected core comprising of

between 1% and 10% of the highest degree nodes such that removing this core completely disconnects the graph.

Sub logarithmic growth

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Densely connected core the graphs have a densely connected core comprising of

between 1% and 10% of the highest degree nodes such that removing this core completely disconnects the graph.

Sub logarithmic growth

Implications of densely connected core

Network contains dense core of users Core necessary for connectivity of 90% of users Most short paths pass through core Could be used for quickly disseminating information

So 10% at core What about remaining nodes (90% at fringe)

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What does the structure look like

the networks contain a densely connected core of high-degree nodes;

and that this core links small groups of strongly clustered, low-degree nodes at the fringes of the network.octopus

Mixing time

Random walk: choose each hop randomly Mixing time: #hops until uniform probability Fast mixing network: mixing time = O(log n)

Sampling by random walks

A random walk has o(1) chance of escaping* True when g bounded by o(n/log n) Of r walks, (1-o(1))r = Ω(r) end nodes are good! Can’t distinguish good from bad nodes in set

Honest region

Sybil region

escapingpaths

non-escaping path

Creating Social Link Is Hard

Social links maintained over Internet

Social network

…

Sybil region

Social networkHonest region

…

Attack edges

A malicious user fools an honest userCreates an attack edge

Sybil resilience & group attachment theory

Sybil schemes find bond groups around a trusted node But, these are only a fraction of all honest nodes Bond groups are hard for Sybils to infiltrate Not the case with identity groups

SYBILGUARDYu, Kaminsky, Gibbons, Flaxman, Sigcomm 2006

Problem Formulation and Objective Social network

» n honest human users» 1+ malicious users : multiple sybil identities» SybilGuard enables an honest node to identify other

nodes» Verifier node V can verify if suspect node S is malicious

SybilGuard Guaranteed bound on number of sybil groups

» Divides n nodes into m equivalence classes» A group is sybil if it contains 1+ sybil nodes

Guaranteed bound on size of sybil groups» In a group, at most w sybil nodes

Completely decentralized» An honest node accepts honest nodes with high probability» Rejects malicious nodes with high probability» Accepts bounded number of sybil nodes

Random Routes Foundation of SybilGuard: different from random walk Random route begins at a random edge of a node At every node

» For an incoming edge i, there is a unique outgoing edge j» Thus, input to output is one-to-one mapped

A node A with d neighbors uniformly randomly chooses a permutation “x1,x2, . . . ,xd” among all permutations of 1,2, . . . ,d.

If a random route comes from the ith edge, A uses edge xi as the next hop.

SybilGuard Algorithm Attack Model

n honest users: One identity/node each Malicious users: Multiple identities each (sybil nodes)

node A: verify node B A computes d random routes (length w) B computes d random routes (length w) If d/2 random routes intersects, accept S Else reject S

If few attack edges, then a sybil node’s random route is less likely to reach honest region

And vice-versa

Main Assumptions of SybilGuard

Honest Nodes

Sybil Nodes

Attackedges

Properties of Random Routes Convergence

» Once two routes merge, they will remain merged Routes are back-traceable There can be only one route with length w that

traverses e along the given direction at its ith hop If two random routes ever share an edge in the same

direction, then one of them must start in the middle of the other

Cycles can exist, but with low probability» Prob. (diameter k cycle) = 1/d(k-2)

Sybilguard Algorithm

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Step 1:Bootstrap the network.

All users exchange signed keys.

Key exchange implies that both parties are human and trustworthy.

Steps: 2

Choose a verifier (A) and a suspect (B).

A and B send out random walks of a certain length (2).

Look for intersections.

A knows B is not a Sybil because multiple paths intersect and they do so at different nodes.

A

B

SybilGuard Algorithm, cont.

33

A

B

SybilGuard Caveats Bootstrapping requires human interaction. Assumes short random walks lie mostly in the honest

regionResults in poor threshold to colluding attackers. In a million node network ,each attack edge

accepts nearly 2000 sybil nodes. In million node network , SybilGuard cannot

bound the number of sybils at all if there are > 15,000 attack edges .

SybilLimitA Near-Optimal Social Network Defense Against Sybil Attacks

SybilLimitA Near-Optimal Social Network Defense Against Sybil Attacks

Motivation : To mitigate the problems of SybilGuard. Basic insight : Social network (same as SybilGuard) SybilLimit Novelity :

1. use many random routes but shorter ones.2. intersect edges not nodes3. limit how often each edge is used.

Identity Registration Each node (honest or sybil) has a locally generated public/private key pair

“Identity”: V accepts S means V accepts S’s public key KS

NO assumption/need PKI

Every suspect S “registers” KS on some other nodes

Registration Goals Ensure that sybil nodes (collectively)

register only on limited number of honest nodes

Still provide enough “registration opportunities” for honest nodes

sybil regionhonest region

K: registered keys of sybil nodes

K K

K

KK

K

K K

K

K

K

K

K

KK K

K: registered keys of honest nodes

Acceptance Criteria

Accept S only if KS is register on

sufficiently many honest nodes K K

K

KK

K

K K

K

K

K

K

K

KK K

sybil regionhonest region

K: registered keys of sybil nodes

K: registered keys of honest nodes

)(logn Take random “walks” of w= hops Honest nodes: likely to remain in honest region* Sybil nodes: must cross an attack edge to reach honest region

Key Idea

sybil regionhonest region

K K

K

KK

K

K K

K

K

K

K

K

KK K

• Register key at last hop of “walk”

4. Is KS registered?

Verification Procedure

VS

1. request S’s set of tails AB

CDEF

F

2. I have three tails

AB; CD; EF

3.common tail: EF

5. Yes. 4 messages involved

V accepts S Tails intersect + key registered

Attack edges SybilGuard SybilLimit

nng log/

nnOg log/ )log( nng )log( ng

)log( ng unbounded

Sybil nodes accepted

unbounded unbounded

nn log/ nnO log/

g between

and

g

SybilInfer: How to Win the Zombie Wars!

Prateek Mittal, George Danezis (MSRC Intern) (MSR Cambridge)

SybilInfer Work from UIUC and Microsoft Research A centralized algorithm Uses the fast mixing properties of social network to

design a Bayesian Classifier Classify nodes

Formal Model

Assign probabilities of cuts being honest

Using Bayes Theorem, we have that :

Next Challenge: Model

Z

HonestXPHonestXTPTHonestXP

)()|()|(

)|( THonestXP

VXHonestXPHonestXTPZ )()|(

)|( HonestXTP

Formal Model

XX XXXX

XXXX

XXXX

XX probprobprobprob NNNNhonestXTP )|(

XXprobXXprob

XXprob

XXprob

XXXX EV

prob ||

1

XXXX EV

prob ||

1

X

X

SYBIL PROOF DHT

Distributed Hash Table

Interface: PUT(key, value), GET(key)→value Route to peer responsible for key

GET( sip://alice@foo )

PUT( sip://alice@foo, 18.26.4.9 )

DHTs are subject to the Sybil attack

Attacker creates many pseudonyms

Disrupts routing or stabilization

s

t

{IDt}

The Sybil attack on open DHTs

Brute-force attack Clustering attack

Sybil Proof DHT How to build a sybil resilient DHT ?

Works from MIT PDOS Group Parallel and Distributed Operating Systems Quest to build Sybil Proof DHT

Sybil-resistant DHT routing 2005 A Sybil proof One hop DHT SocialNets 2008 Whanau NSDI 2010

A Sybil proof one hop DHT Motivation: SybilGuard/SybilLimit:Not a DHT, but a “general” Sybil defense Honest node accepts at most O(g log n) SybilsFeatures : DHTs are subject to the Sybil attack Social networks provide useful information Created a Sybil-resistant one-hop DHT

Resistant to g = o(n/log n) attack edges Table sizes and routing BW O(√n log n) Uses O(1) messages to route

Basic one-hop DHT design Construct finger table by r random walks Route to t by asking all fingers about t

If r = Ω(√n log n), some finger knows t WHP Adversary cannot interfere with routing

s

t

r

r

{know t?} {know t?}{t?} {t?} {t?} {t?} {t?}

{t’s IP address}

{forwarded message from s}

Properties of this solution Finger table size: r = O( ) Bandwidth to construct: O(r log n) bits Bandwidth to query: O(r) messages Probability of failure: 1/poly(n)

nn log

WHĀNAU: A SYBIL-PROOF DISTRIBUTED HASH TABLE

Chris Lesniewski-Laas M. Frans Kaashoek NSDI 2010

Contribution

Whānau: an efficient Sybil-proof DHT protocol GET cost: O(1) messages, one RTT latency Cost to build routing tables: O(√N log N)

storage/bandwidth per node (for N keys) Oblivious to number of Sybils!

Proof of correctness PlanetLab implementation Large-scale simulations vs. powerful attack

Sybil regio

n

Social networkHonest region

…

Attack edges

Random walksc.f. SybilLimit [Yu et al 2008]

Building tables using random walksc.f. SybilLimit [Yu et al 2008]

What have we accomplished?• Small fraction (e.g. < 50%) of

bad nodes in routing tables• Bad fraction is independent

of number of Sybil nodes

SETUP LOOKUP

Social Network

Routing Tables

key

value

PUT(key, value)PUT Queue

Routing table structure O(√n) fingers and O(√n) keys stored per node Fingers have random IDs, cover all keys WHP Lookup: query closest finger to target key

Finger tables: (ID, address)

Key tables: (key,value)

Keynes

AardvarkZyzzyva

Kelvin

From social network to routing tables

Finger table: randomly sample O(√n) nodes Most samples are honest

Honest nodes pick IDs uniformly

Plenty of fingers near key

Sybil ID clustering attack

[Hypothetical scenario: 50% Sybil IDs, 50% honest IDs]

Many bad fingers near key

Honest layered IDs mimic Sybil IDsLayer 0 Layer 1

Every range is balanced in some layerLayer 0 Layer 1

Two layers is not quite enoughLayer 0 Layer 1

Ratio =1 honest :10 Sybils

Ratio =10 honest :100 Sybils

Log n parallel layers is enough

log n layered IDs for each node Lookup steps:

1. Pick a random layer2. Pick a finger to query3. GOTO 1 until success or timeout

Layer 0 Layer 1 Layer 2 Layer L

…

From Social relations to Routing Tables

SETUP LOOKUP

Social Network Routing Tables

key

value

PUT(key, value)

PUT Queue

Problems Whanau’s goal is to create a Sybil proof DHT Which ensures delivery Whanau uses the idea of random walk in fast mixing

graphs Whanau has changed the basic structure of DHT Tables contain O(√n log n) entries !! The DHT has become a one hop DHT But O(√n) entries are insane !! Think of a DHT with 100000000 users

How to handle churn ??

OUR IDEA OF A SYBIL PROOF DHT

Our Idea We are given a social graph Each node knows about their friends in the social

graph Same assumptions about SybilGuard or Whanau

Fast mixing graphs Small cut around attack edges o(n/log n) attack edges at most

Our Motivation for DHT Isn’t it possible to keep the basic routing features of

a DHT while making it sybil resilient? O(log n) table size Lookup should take O(log n) We should use social information to build the DHT

Bootstrapping the DHT Here comes the fundamental question

How to convert a given social graph into a DHT So that the socially connected nodes are near Socially far nodes are far in the DHT Sybil nodes require significant amount of social

engineering to be strongly connected members of a social group

A new type of DHT We want to build a DHT

Where distance between two nodes in the DHT-Space is related to their social-distance

i.e, two friends in the social graph are expected to be one-hop distant in the DHT-Space

Most of the queries will be through friends Hence, the probability of reaching a Sybil node is less

We use the idea of Plexus A novel DHT routing based on linear block codes Plexus: A Scalable Peer-to-Peer Protocol Enabling Efficient Subset

Search : Reaz Ahmed and Raouf Boutaba ACM/ IEEE TON Feb 2009

85/15

Advertisement, P Query, Q

advSet(P) C qSet(Q) C

PadvSetQqSetPQ

Plexus: Index ClusteringC = set of cluster heads

Linear code, C <n,k,d>

Cluster head Codeword

Generator matrix based routing

knkk

n

n

k ggg

ggg

ggg

g

g

g

G

21

22221

11211

2

1

0111000

1010100

1100010

1110001

0

15

23

47

E

G

<7, 4, 3> Hamming code

Cluster

Pattern

Cluster head

86/15

Linear Binary Code C = <n, k, d> linear binary code

n: number of bits in a codeword k: dimension 2k codeword in code d: minimum distance between any pair of codeword e.g., G24=24, 12, 8

Generator Matrix G,

knkk

n

n

k ggg

ggg

ggg

g

g

g

G

21

22221

11211

2

1

2k codeword can be formed by applying XOR to any combination of these k codewords.

87/15

Plexus: Routing Table In a complete network each peer is responsible for a

codeword Peer with codeword X maintains links to k+1 peers

with IDs computed as: Xi = X gi 1 i k

Xk+1 = X g1 g2 … gk

Xk+1 is used for: Replication Reducing routing cost

88/15

Plexus: Routing Observation: C is closed under operation

tiii gggXYCYX 21

,

532 gggXY

Example: Route from X to Y where,

X Y

X2g2

X3

X5

g3

g5

X23

g3g5

X25

g3

g5

X35 g2g3

g5

g2

g2

X2 X23 X235=YX1=Xg1

X2=Xg2…

Xk=Xgk

X21=X2g1…

X23=X2g3…

X2k=X2gk

X231=X23g1

…X235=X23g5

…X23k=X23gk

kk gggXX 211

89/15

Hamming distance based clustering & indexing Maximum routing hops (within a subnet)

½ K in normal condition

½ K +2 in presence of failure.

Disjoint routing paths Source X destination Y

XY is disjoint from XYK+1

Alternate routing paths Suitable for Multicasting

Improved fault resilience

Improved load balancing

Strengths of Plexus Routing

X

Y

YK+1

Rep

lica

Y’ Y

YK+1Y’K+1X

Social Network to Plexus Now, the problem reduces to assigning appropriate

linear block codes to the nodes How to do that ?

Naïve Idea All nodes u know their friends F1(u). All nodes u send

F1(u) to all of their friends. At this point, Every node u, in addition to F1(u), can

calculate its "mutual friend list" for each of its friends. For any two friends u, v : Their mutual friend set is

Every node u, can also calculate F2(u), its exact two-hop distant friend list.

)()(),( 111 vFuFvuM

}{)())(()( 1112 uuFuFFuF

Naïve Idea Each node u, sorts their friends according to an

"influence metric.“ For each friend v of a node u, Influence(u,v) = Influence of v on u = I (u, v)

it is highly probable that a sybil node will have very low influence on an honest node via attack edge due to very small number of mutual friends. However not only sybils, but also a common friend of two

groups will have low influence on both group (however, this case is not handled in any algorithms)

|)(|

|),(|),(

1

1

uF

vuMvuI

Naïve Idea Each node u, calculates I(u,v) and I(v,u) for all its

friends. There are 2*deg(u) such quantities. C(u) = Those nodes for which u has more influence on v

than v has on u P(u) = Those nodes for which v has more influence on u

than u has on v. and R(u) = Those nodes for which u and v both has same

influence on each other

)},(),(),(:{)(

)},(),(),(:{)(

)},(),(),(:{)(

1

1

1

uvIvuIuFvvuR

uvIvuIuFvvuP

vuIuvIuFvvuC

Naïve Idea max{ C(u) } = x = The friend, on which u is maximum

influential. However, it doesn’t mean x doesn’t have a friend more

influential than u. It means, u does not have a friend on which it has more influence than it has on x.

max { P(u) } = y = The friend which has the highest influence on u. It also doesn’t mean y doesn’t have friends on which it has

more influence than it has on u. max { R(u) } = z = The friends which has same influence

on u as u has on them.

Naïve Idea lx = I(x,u) ; Iy = I(u,y) , Iz = I(u,z) = I(z,u)

MI = { Ix, Iy, Iz}If Max { MI } = Ix : u is an “influencial” nodeIy : u is an “influenced” nodeIz : u is an “neutral” node

Naïve Idea Action D : If u is “influenceD”, it decides not to

generate any ID, and decides to take command from y. It sends a message to y that it has come into his control.

Action L : If u is an influentiaL node, it decides to generate ID for u and some of F1(u) and F2(u)

Action N : If u is Neutral, then decides Action L or Action D by a uniform bernoulli trial.

Now, u generates ID for itself, for those of Gang(u). It will try to keep friend IDs as close as possible, also

those of Gang(u) which are friends themselves will get close ID as possible.

u will inform all of Gang(u) all the ids generated by it. Members of Gang(u) will take care of id generation

of their neighbors

But how to handle collision ? Some gossip protocols needed !!

Naïve Idea Thus Id’s will be assigned in the code space according

to their “Social Groups”