SybilInfer: Detecting Sybil Nodes using Social Networks

SybilInfer is an algorithm for labelling nodes in a social network as honest users or Sybils controlled by an adversary. At the heart of SybilInfer lies a probabilistic model of honest social networks, and an inference engine that returns potential regions of dishonest nodes. The Bayesian inference approach to Sybil detection comes with the advantage label has an assigned probability, indicating its degree of certainty. We prove through analytical results as well as experiments on simulated and real-world network topologies that, given standard constraints on the adversary, SybilInfer is secure, in that it successfully distinguishes between honest and dishonest nodes and is not susceptible to manipulation by the adversary. Furthermore, our results show that SybilInfer outperforms state of the art algorithms, both in being more widely applicable, as well as providing vastly more accurate results.

[1]  W. K. Hastings,et al.  Monte Carlo Sampling Methods Using Markov Chains and Their Applications , 1970 .

[2]  Chris Lesniewski-Laas,et al.  A Sybil-proof one-hop DHT , 2008, SocialNets '08.

[3]  David J. Phillips.  Defending the Boundaries: Identifying and Countering Threats in a Usenet Newsgroup , 1996, Inf. Soc..

[4]  David R. Karger,et al.  Chord: a scalable peer-to-peer lookup protocol for internet applications , 2003, TNET.

[5]  Shishir Nagaraja,et al.  Anonymity in the Wild: Mixes on Unstructured Networks , 2007, Privacy Enhancing Technologies.

[6]  Robert Harper.  Self-adjusting computation , 2004, LICS 2004.

[7]  Nick Mathewson,et al.  Tor: The Second-Generation Onion Router , 2004, USENIX Security Symposium.

[8]  Frank Dabek,et al.  A cooperative file system , 2001 .

[9]  Peter Palfrader,et al.  Mixmaster protocol --- version 2 , 2000 .

[10]  George Danezis,et al.  Sybil-Resistant DHT Routing , 2005, ESORICS.

[11]  Santosh S. Vempala,et al.  On clusterings-good, bad and spectral , 2000, Proceedings 41st Annual Symposium on Foundations of Computer Science.

[12]  Alexander Aiken,et al.  Attack-Resistant Trust Metrics for Public Key Certification , 1998, USENIX Security Symposium.

[13]  David J. C. MacKay,et al.  Information Theory, Inference, and Learning Algorithms , 2004, IEEE Transactions on Information Theory.

[14]  Ian T. Foster,et al.  Mapping the Gnutella Network: Properties of Large-Scale Peer-to-Peer Systems and Implications for System Design , 2002, ArXiv.

[15]  Robert Harper,et al.  Self-adjusting computation , 2004, Proceedings of the 19th Annual IEEE Symposium on Logic in Computer Science, 2004..

[16]  John R. Douceur,et al.  The Sybil Attack , 2002, IPTPS.

[17]  Feng Xiao,et al.  SybilLimit: A Near-Optimal Social Network Defense against Sybil Attacks , 2008, 2008 IEEE Symposium on Security and Privacy (sp 2008).

[18]  Leslie Lamport,et al.  The Byzantine Generals Problem , 1982, TOPL.

[19]  Miguel Castro,et al.  Secure routing for structured peer-to-peer overlay networks , 2002, OSDI '02.

[20]  U Moeller,et al.  Mixmaster Protocol Version 2 , 2004 .

[21]  Michael Kaminsky,et al.  SybilGuard: Defending Against Sybil Attacks via Social Networks , 2008, IEEE/ACM Transactions on Networking.

[22]  S. Berg.  Snowball Sampling—I , 2006 .

[23]  Sharon L. Milgram,et al.  The Small World Problem , 1967 .

[24]  Baruch Awerbuch,et al.  Optimal distributed algorithms for minimum weight spanning tree, counting, leader election, and related problems , 1987, STOC.

[25]  George Danezis,et al.  Mixminion: design of a type III anonymous remailer protocol , 2003, 2003 Symposium on Security and Privacy, 2003..

[26]  John Langford,et al.  CAPTCHA: Using Hard AI Problems for Security , 2003, EUROCRYPT.

[27]  Dana Randall,et al.  Rapidly mixing Markov chains with applications in computer science and physics , 2006, Computing in Science & Engineering.