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Leveraging Social Networks Against Sybil Attacks in Distributed Systems

Learn about Sybil attacks in online services like social networks, defend strategies using social networks, and limitations of current detection schemes.

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Leveraging Social Networks Against Sybil Attacks in Distributed Systems

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  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 • RESOURCE2: 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 canonly 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? Trusted Node Trusted Node • 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?

  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. Destination Source Destination 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 x x

  19. { Multiple Identities 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

  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?

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