SybilGuard: defending against sybil attacks via social networks

Peer-to-peer and other decentralized, distributed systems are known to be particularly vulnerable to sybil attacks. In a sybil attack, a malicious user obtains multiple fake identities and pretends to be multiple, distinct nodes in the system. By controlling a large fraction of the nodes in the system, the malicious user is able to "out vote" the honest users in collaborative tasks such as Byzantine failure defenses. This paper presents SybilGuard, a novel protocol for limiting the corruptive influences of sybil attacks. Our protocol is based on the "social network" among user identities, where an edge between two identities indicates a human-established trust relationship. Malicious users can create many identities but few trust relationships. Thus, there is a disproportionately small "cut" in the graph between the sybil nodes and the honest nodes. SybilGuard exploits this property to bound the number of identities a malicious user can create. We show the effectiveness of SybilGuard both analytically and experimentally.

[1]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[2]  Marvin Theimer,et al.  Feasibility of a serverless distributed file system deployed on an existing set of desktop PCs , 2000, SIGMETRICS '00.

[3]  Jon M. Kleinberg,et al.  The small-world phenomenon: an algorithmic perspective , 2000, STOC '00.

[4]  Robert Morris,et al.  Chord: A scalable peer-to-peer lookup service for internet applications , 2001, SIGCOMM 2001.

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

[6]  Hui Zhang,et al.  Predicting Internet network distance with coordinates-based approaches , 2002, Proceedings.Twenty-First Annual Joint Conference of the IEEE Computer and Communications Societies.

[7]  David A. Wagner,et al.  Secure verification of location claims , 2003, WiSe '03.

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

[9]  Matthew Richardson,et al.  Trust Management for the Semantic Web , 2003, SEMWEB.

[10]  A. Perrig,et al.  The Sybil attack in sensor networks: analysis & defenses , 2004, Third International Symposium on Information Processing in Sensor Networks, 2004. IPSN 2004.

[11]  Ion Stoica,et al.  Robust incentive techniques for peer-to-peer networks , 2004, EC '04.

[12]  Ittai Abraham,et al.  Probabilistic quorums for dynamic systems , 2003, Distributed Computing.

[13]  Michael Mitzenmacher,et al.  Probability And Computing , 2005 .

[14]  Alice Cheng,et al.  Sybilproof reputation mechanisms , 2005, P2PECON '05.

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

[16]  Mary Baker,et al.  The LOCKSS peer-to-peer digital preservation system , 2005, TOCS.

[17]  Rida A. Bazzi,et al.  On the establishment of distinct identities in overlay networks , 2005, PODC.

[18]  Stephen P. Boyd,et al.  Gossip algorithms: design, analysis and applications , 2005, Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies..

[19]  Adrian Perrig,et al.  Distributed detection of node replication attacks in sensor networks , 2005, 2005 IEEE Symposium on Security and Privacy (S&P'05).

[20]  Emin Gün Sirer,et al.  Experience with an Object Reputation System for Peer-to-Peer Filesharing , 2006, NSDI.

[21]  Abraham D. Flaxman.  Expansion and Lack Thereof in Randomly Perturbed Graphs , 2006, WAW.

[22]  Nick Feamster,et al.  Understanding the network-level behavior of spammers , 2006, SIGCOMM 2006.

[23]  Aravind Srinivasan,et al.  Efficient lookup on unstructured topologies , 2007, IEEE J. Sel. Areas Commun..

[24]  Aravind Srinivasan,et al.  Efficient lookup on unstructured topologies , 2005, IEEE Journal on Selected Areas in Communications.

[25]  Krishna P. Gummadi,et al.  Ostra: Leveraging Trust to Thwart Unwanted Communication , 2008, NSDI.

[26]  Michael Kaminsky,et al.  SybilLimit: A Near-Optimal Social Network Defense against Sybil Attacks , 2008, S&P 2008.