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Issue No.12 - Dec. (2013 vol.24)
pp: 2492-2502
Wei Wei , The College of William and Mary, Williamsburg
Fengyuan Xu , The College of William and Mary, Williamsburg
Chiu C. Tan , Temple University, Philadelphia
Qun Li , The College of William and Mary, Williamsburg
ABSTRACT
Distributed systems without trusted identities are particularly vulnerable to sybil attacks, where an adversary creates multiple bogus identities to compromise the running of the system. This paper presents SybilDefender, a sybil defense mechanism that leverages the network topologies to defend against sybil attacks in social networks. Based on performing a limited number of random walks within the social graphs, SybilDefender is efficient and scalable to large social networks. Our experiments on two 3,000,000 node real-world social topologies show that SybilDefender outperforms the state of the art by more than 10 times in both accuracy and running time. SybilDefender can effectively identify the sybil nodes and detect the sybil community around a sybil node, even when the number of sybil nodes introduced by each attack edge is close to the theoretically detectable lower bound. Besides, we propose two approaches to limiting the number of attack edges in online social networks. The survey results of our Facebook application show that the assumption made by previous work that all the relationships in social networks are trusted does not apply to online social networks, and it is feasible to limit the number of attack edges in online social networks by relationship rating.
INDEX TERMS
Social network services, Image edge detection, Detection algorithms, Algorithm design and analysis, Network topology,random walk, Sybil attack, social network
CITATION
Wei Wei, Fengyuan Xu, Chiu C. Tan, Qun Li, "SybilDefender: A Defense Mechanism for Sybil Attacks in Large Social Networks", IEEE Transactions on Parallel & Distributed Systems, vol.24, no. 12, pp. 2492-2502, Dec. 2013, doi:10.1109/TPDS.2013.9
REFERENCES
[1] J.R. Douceur, "The Sybil Attack," Proc. Revised Papers First Int'l Workshop Peer-to-Peer Systems (IPTPS '01), 2002.
[2] E. Novak and Q. Li, "A Survey of Security and Privacy Research in Online Social Networks," Technical Report WM-CS-2012-2, College of William and Mary, 2012.
[3] G. Danezis and P. Mit, "Sybilinfer: Detecting Sybil Nodes Using Social Networks," Proc. Network and Distributed System Security Symp. (NDSS), 2009.
[4] N. Tran, J. Li, L. Subramanian, and S.S. Chow, "Optimal Sybil-Resilient Node Admission Control," Proc. IEEE INFOCOM, 2011.
[5] L. Xu, S. Chainan, H. Takizawa, and H. Kobayashi, "Resisting Sybil Attack by Social Network and Network Clustering," Proc. IEEE/IPSJ 10th Int'l Symp. Applications and Internet (SAINT), 2010.
[6] H. Yu, P.B. Gibbons, M. Kaminsky, and F. Xiao, "SybilLimit: A Near-Optimal Social Network Defense against Sybil Attacks," Proc. IEEE Symp. Security and Privacy, 2008.
[7] H. Yu, M. Kaminsky, P.B. Gibbons, and A. Flaxman, "SybilGuard: Defending against Sybil Attacks via Social Networks," Proc. ACM SIGCOMM, 2006.
[8] L. Bilge, T. Strufe, D. Balzarotti, and E. Kirda, "All Your Contacts Are Belong to Us: Automated Identity Theft Attacks on Social Networks," Proc. 18th Int'l Conf. World Wide Web (WWW '09), 2009.
[9] W. Wei, F. Xu, C.C. Tan, and Q. Li, "SybilDefender: Defend against Sybil Attacks in Large Social Networks," Proc. IEEE INFOCOM, 2012.
[10] R.S. Peterson and E.G. Sirer, "AntFarm: Efficient Content Distribution with Managed Swarms," Proc. Networked Systems Design and Implementation (NSDI), 2009.
[11] H. Yu, M. Kaminsky, P.B. Gibbons, and A. Flaxman, "Sybilguard: Defending against Sybil Attacks via Social Networks," Technical Report IRP-TR-06-01, Intel Research Pittsburgh, 2006.
[12] R. Kannan, S. Vempala, and A. Vetta, "On Clusterings: Good, Bad and Spectral," Proc. 41st Ann. Symp. Foundations Computer Science (FOCS), 2000.
[13] B. Viswanath, A. Mislove, M. Cha, and K.P. Gummadi, "On the Evolution of User Interaction in Facebook," Proc. Second ACM Workshop Online Social Networks (WOSN), 2009.
[14] C. Wilson, B. Boe, A. Sala, K.P.N. Puttaswamy, and B.Y. Zhao, "User Interactions in Social Networks and Their Implications," Proc. Fourth ACM European Conf. Computer Systems (EuroSys), 2009.
[15] Y. Boshmaf, I. Muslukhov, K. Beznosov, and M. Ripeanu, "The Socialbot Network: When Bots Socialize for Fame and Money," Proc. 27th Ann. Computer Security Applications Conf. (ACSAC), 2011.
[16] A. Mislove, M. Marcon, K.P. Gummadi, P. Druschel, and B. Bhattacharjee, "Measurement and Analysis of Online Social Networks," Proc. Seventh ACM SIGCOMM Conf. Internet Measurement (ACM/USENIX IMC), 2007.
[17] R. Albert and A. Barabási, "Statistical Mechanics of Complex Networks," Rev. Modern Physics, vol. 74, pp. 47-97, 2002.
[18] P. Erdös and A. Rényi, "On Random Graphs," Publicationes Mathemticae (Debrecen), vol. 6, pp. 290-297, 1959.
[19] A. Sala, L. Cao, C. Wilson, R. Zablit, H. Zheng, and B.Y. Zhao, "Measurement-Calibrated Graph Models for Social Network Experiments," Proc. 19th Int'l Conf. World Wide Web (WWW '10), 2010.
[20] B. Viswanath, A. Post, K.P. Gummadi, and A. Mislove, "An Analysis of Social Network-Based Sybil Defenses," Proc. ACM SIGCOMM, 2010.
[21] J. Kleinberg, "The Small-World Phenomenon: An Algorithm Perspective," Proc. 32nd Ann. ACM Symp. Theory Computing (STOC '00), 2000.
[22] J. Davidsen, H. Ebel, and S. Bornholdt, "Emergence of a Small World from Local Interactions: Modeling Acquaintance Networks," Physical Rev. Letters, vol. 88, 2002.
[23] "Trust Network Data Sets," http://www.trustlet.org/wiki-Trust_network_datasets , 2013.
[24] M.E.J. Newman, "The Structure of Scientific Collaboration Networks," Proc. Nat'l Academy Sciences USA, vol. 98, no. 2, pp. 404-409, 2001.
[25] A. Mohaisen, N. Hopper, and Y. Kim, "Keep Your Friends Close: Incorporating Trust into Social Network-Based Sybil Defenses," Proc. IEEE INFOCOM, 2011.
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