SybilGuard: Defending Against Sybil Attacks via Social Networks

1. SybilGuard: Defending Against Sybil Attacks via Social Networks Haifeng Yu Michael Kaminsky Phillip B. Gibbons Abraham Flaxman Presented by John Mak, Janet Yung

2. Outline • Introduction to Sybil Attack • Model & Problem formulation • Sybil Guard Overview • Simulation Result & Analysis • Conclusion • Our views

3. Introduction to Sybil Attack • P2p and decentralized, distributed systems particularly vulnerable • Malicious user obtains multiple fake identities • Gain large influence by “out vote” honest users

4. Introduction to Sybil Attack • Malicious user obtains multiple fake identities • Malicious behavior becomes a norm (e.g. Byzantine failures) • Many protocols assume < 1/3 malicious nodes • Easily create 1/3 nodes  Break defense

5. Introduction to Sybil Attack • Centralized authority: • Control Sybil attack easily • Verify real life credential • Hard for worldwide to trust • Single point of failure – bottleneck, DOS • Scare away potential users – requires sensitive information

6. Introduction to Sybil Attack • Decentralized approach is hard: • Harvest (Steal) IP addresses • No common IP prefix  Hard to filter • Advertise BGP route on unused block of IP address • Botnet - Co-opt large number of end-user machines

7. Introduction to Sybil Attack • Not very successful defense approaches: • Resource-challenge approach (computational puzzles) • Network coordinates • Reputation system based on historical behavior

8. Model & Problem Formulation • Users: • n honest users: single identity • 1+ malicious user: multiple identities • Identity: • Also called “node” • Sybil identity: malicious user’s identity • Defense system • Verifier node V accept another node S • Ideally, V only accept honest nodes.

9. Model & Problem Formulation • Bounding no. of sybil groups • Divide all nodes into at most g equivalence groups • Sybil Group: equivalence group contains at least one Sybil node • Defense system guarantees no. of groups, without need to know if the group is sybil

10. Model & Problem Formulation • Bounding size of Sybil Group • at most w nodes in a group • limit no. of sybil nodes accepted each node can accept • Summary • decentralized • honest node accepts, and is accepted by most other honest nodes • honest node accepts a bounded number of sybil nodes.

11. Social Network • Consists of users (nodes) • Human established trust relationships • Nodes connected by an edge (friend) • Real life relationship can bound both the number and size of sybil groups • Usually degree ~ 30 • Malicious user fools honest user to trust him/her • an attack edge connected a malicious user and an honest user

12. SybilGuard Overview • Ensures honest user share at most one edge with sybil nodes created by a malicious user • A protocol enables honest nodes to accept a large fraction of the other honest nodes • SybilGuard does not increase or decrease the number of edges in the social network as a result of its execution

13. SybilGuard Overview • Random routes direct random walk for all nodes • Pre-computed random permutation • one-to-one mapping from incoming edges to out-going edges • Random routes • convergence property • back-traceable property • Multiple random routes of a certain length (w)

14. Random route • Basis of SybilGuard • Honest node (verifier) decides whether or not to accept another node (suspect) • Honest node’s random route • highly likely to stay within the honest region • Highly likely to intersect within (w) steps • If there are (g) attack edges, the number of sybil groups is bounded by (g)

15. Fast mixing property • Assume social networks tend to be fast mixing, which necessarily means that subsets of honest nodes have good connectivity to the rest of the social network • Assume the verifier is itself an honest node

16. Attack edge

17. Key exchange • Each pair of friendly nodes shares a unique symmetric secret key (password) called the edge key • Key distribution is done out-of-band • Each honest node constrains its degree within some constant (e.g. 30) in order to prevent the adversary from increasing the number of attack edges (g) dramatically

18. Limits attack edges • Limited number of attack edges (g) • Adding new attack edge needs out-of-band verification • Malicious users: • Hard to convince honest users to be friends • Quite difficult to do on a large scale

19. Common ways adversary may use to increase g • Befriending with honest users in real life • Convince honest node to accept sybil nodes as friends • Compromises a large fraction of nodes in the system. • The adversary does not even need to launch a sybil attack. SybilGuard will not help here. • Botnet • Challenging to acquire a botnet containing many nodes that already in the system.

20. Random route • Convergence property • Once two routes traverse the same edge along the same direction, they will merge and stay merged (i.e. the convergence property) • Back-traceableproperty • Using a permutation as the routing table further guarantees that the random routes are back-traceable • There can be only one route with length (w) that traverses the same section of route (e)

21. Problems of random route • Loop (same edge more than once) • Unlikely to form in a fast mixing graph • Enters the sybil region • Unlikely according to:THEOREM 1. For any connected and non-bipartite social network,the probability that a length-w random walk starting froma uniformly random honest node will ever traverse any of theg attack edges is upper bounded by gw/n. In particular, whenw = Θ(√nlogn) and g = o(√n/logn), this probability is o(1).

22. SybilGuard Design • Use redundancy • Instead of performing one random route • A node with degree (d) performs random routes along each of its edges • Verifier V accept suspect S • If exist d/2 routes from the verifier node • One of V’s route accept S if that route intersect with one of S’s route

23. Registry table • Each node will maintain and propagate one’s registry tables and witness tables to its neighbors • SybilGuard requires a node to register with all (w) nodes along each of its routes by using public key cryptography • When a verifier V wants to verify S, V will ask the intersection point between S’s route and V’s route whether S is indeed registered

24. Registry & Witness tables

25. Bandwidth consumption • Studying a one million nodes social network • w=2000 • Data sent by each node for registry table is small • Bandwidth consumption acceptable • since the registry table updates are needed only when social trust relationships change

26. Witness table • Propagated and updated in a similar fashion as the registry table • Backward • Updated when a node’s IP address changes • Can be done lazily in the verification process

27. Verify process • For a node V to verify a node S • find the intersection nodes for all of its routes by the witness tables downstream • Authenticates the intersection node one by one by the private key • Ask that node to check if S’s public key is store in one of its registry tables. • If S’s public key is found, that route of V will accept S

28. Verify Process • If more than half of the route from V accept S, • node V will accept node S • V will interact will S later by request S to encrypt its message by its private key • For the sybil nodes region with (g) attack edges, • Polluted entries in registry tables bounded by g*w*w/2 • still less than half of the total number of entries n*d*w • even with g*w tends to (n) with (d) being the degree of each node (d >= 2) and (n) being the total number of nodes

29. Route length w Constraints: • Must be sufficiently small to ensure remains entirely within the honest region • Must be sufficiently large to ensure that routes will intersect with high probability • w related to n • Challenging because we do not know n for a decentralized system

30. Route length w • Determine locally by sampling • Node A performs short random walk (e.g. 10 hops) at node B • Assume B is honest (with high probability) • A checks no. of hops for intersection with their random routes • A asks for the witness tables from B. • Repeat above, calculate median value.

31. Sybil Guard under Dynamics • Bypass offline nodes • V verify other node S • Probably multiple intersection points • V have at least one intersection point online • Propagate registry & witness tables • User creation / deletion / ip address change • Infrequent changes • Lookahead route table • Store information of next K hops

32. Sybil Guard under Dynamics • Incremental routing table maintenance • Instead of re-create a new permutation • Make changes in current permutation • Add • X1 X2  X3  X4  (insert at end) • X1 X2  (insert here)  X4  X3 • Delete “3” • Before: X1 X2  X3  X4  X5 • After: X1 X2  X5  X4

33. Attacks Exploiting Node Dynamics • Potential attacks under Node Dynamics • Malicious user M change public key to key2 • Suppose DABC • Suppose revoke key1 • Random routes along all directions • D’s key3 will overwrite key2

34. Probability of Intersection • Kleinberg’s synthetic social network model • a million-node graph with average node degree of 24

35. Results with no Sybil Attackers • Probability of random routes being loops • Loop reduces effective length of random route • Loop is very rare • 99.3% of the routes do not form loops in their first 2500 hops

36. Results with no Sybil Attackers • Probably of honest node being accepted • at least one intersection point online • If at least 10 online/offline intersection points  verification succeeds • In 1 million-node graph • w = 300 • probability = 99.96% having >=10 intersections

37. Results with no Sybil Attackers • Estimate random route length w • Sampling technique to determine w • Node A choose a node B to determine w • Node B – not necessarily uniformly random • Need to re-estimate daily

38. Probability of routes in honest region • 1 million-node graph • 100% for g <=2000; 99.8% for g=2500 • 0.2% -- Nodes befriending with sybil attackers

39. Probability of honest nodes being accepted • Still 99.8% with 2500 attack edges • Redundancy is necessary

40. Our views • Hard to link real life to virtual network? • My real life friends may not join the virtual network • Maybe centralized authentication better? • 99.8% honest nodes accepted, but 0.2% not accepted. • The 0.2% is honest

41. Others’ views • Fast mixing assumption in social network • Japanese’s social network may not mix with US social network?