1. Leveraging Social Networks to Defend against Sybil attacks Krishna Gummadi Networked Systems Research Group Max Planck Institute for Software Systems Germany

2. Sybil attack Fundamental problem in distributed systems Attacker creates many fake identities (Sybils) –Used to manipulate the system Many online services vulnerable –Webmail, social networks, p2p Several observed instances of Sybil attacks –Ex. Content voting tampered on YouTube, Digg

3. Sybil defense approaches Tie identities to resources that are hard to forge or obtain RESOURCE 1: Certification from trusted authorities –Ex. Passport, social security numbers –Users tend to resist such techniques RESOURCE 2: Resource challenges (e.g., crypto-puzzles) –Vulnerable to attackers with significant resources –Ex. Botnets, renting cloud computing resources RESOURCE 3: Links in a social network?

4. Using social networks to detect Sybils Assumption: Links to good users hard to form and maintain –Users mostly link to others they recognize Attacker can only create limited links to non-Sybil users Leverage the topological feature introduced by sparse set of links

5. Social network-based Sybil detection Very active area of research –Many schemes proposed over past five years Examples: –SybilGuard [SIGCOMM’06] –SybilLimit [Oakland S&P ’08] –SybilInfer [NDSS’08] –SumUp [NSDI’09] –Whanau [NSDI’10] –MOBID [INFOCOM’10]

6. But, many unanswered questions All schemes make two common assumptions –Honest nodes: they are fast mixing –Sybils: they do not mix quickly with honest nodes But, each uses a different graph analysis algorithm –Unclear relationship between schemes Is there a common insight across the schemes? –Is there a common structural property these schemes rely on? Such an insight is necessary to understand –How well would these schemes work in practice? –Are there any fundamental limitations of Sybil detection?

7. Common insight across schemes All schemes find local communities around trusted nodes –Roughly, set of nodes more tightly knit than surrounding graph Accept service from those within the community –Block service from the rest of the nodes

8. Are certain network structures more vulnerable? When honest nodes divide themselves into multiple communities –Cannot tell apart Sybils & non-Sybils in a distant community How often do social networks exhibit such community structures? Trusted Node

9. How often do non-Sybils form one cohesive community? Not often! Many real-world social networks have high modularity –They exhibit multiple well-defined community structures

10. Facebook RICE undergraduates’ network Exhibits densely connected user communities within the graph Other social networks have even higher modularity

11. How often do non-Sybils form one cohesive community? Traditional methodology: –Analyze several real-world social network graphs –Generalize the results to the universe of social networks A more scientific method: –Leverage insights from sociological theories on communities –Test if their predictions hold in online social networks –And then generalize the findings

12. Group attachment theory Explains how humans join and relate to groups Common-identity based groups –Membership based on self interest or ideology –E.g., NRA, Greenpeace, and PETA –Tend to be loosely-knit and less cohesive Common-bond based groups –Membership based on inter-personal ties, e.g., family or kinship –Tend to form tightly-knit communities within the network

13. Dunbar’s theory 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 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 Limits the size of cohesive common-bond based groups

14. Prediction and implication Strongly cohesive communities in real-world social networks will be necessarily small –No larger than a few hundred nodes! If true, it imposes a limit on the number of non-Sybils we can detect with high accuracy –Will be problematic as social networks grow large

15. Verifying the prediction In all networks, groups larger than a few 100 nodes do not remain cohesive Small cohesive groups tend to be family and alumni groups Large groups are often on abstract topics like music or politics Real-world data sets analyzed

16. Implications Fundamental limits on social network-based Sybil detection Can reliably identify only a limited number of honest nodes In large networks, limits interactions to a small subset of honest nodes –Might still be useful in certain scenarios, e.g., white listing email from friends But, what to do with nodes not in the honest node subset?

17. One way forward: Sybil tolerance Rather than detect bad nodes, lets limit bad behavior Sybil detection: Use network to find Sybil nodes –Accept / receive unlimited service from non-Sybils –Refuse to interact with Sybils Sybil tolerance: Use network to limit nodes’ privileges –Interact with all nodes, but monitor their behavior –Limit bad behavior from any node, Sybil or non-Sybil

18. x Destination Source Destination x Illustrative example: Applying Sybil tolerance to email spam Key idea: Link privileges to credit on network links –Once the credit is exhausted, the node stops receiving service –Does not matter if the node is a Sybil or not

19. Illustrative example: Applying Sybil tolerance to email spam Creating multiple node identities does not help –So long as they cannot create links to arbitrary honest nodes No assumption about connectivity between non-Sybils { Multiple Identities

20. Such Sybil tolerant systems already exist Ostra [NSDI’08]: Limiting unwanted communication SumUp [NSDI’09]: Sybil-resilient voting Their properties were not well understood before

21. Sybil detection versus tolerance Sybil detection –Assumes network of honest nodes is fast mixing –Does not require anything beyond network topology Sybil tolerance –No assumption about connectivity between honest nodes –Requires user behavior to be monitored and labeled

22. Summary: A comprehensive approach to social network-based Sybil defense Think beyond good and evil Sybil tolerance complements Sybil detection –Use Sybil detection to white list nodes in local communities of trusted nodes –Use Sybil tolerance when interacting with nodes outside the local communities Currently exploring applications of the approach –E.g., to deter site crawlers

23. Thank you! Questions?