Graph Based Real Sybil Detection in OSNs

IJCSEC Front Page

In current generation, the rapid growth of Online Social Networks (OSNs) have made far reaching effect on everyone’s social life. This increase in popularity, usage and anonymous nature of OSNs exposed the possibility of being attacked. In Sybil attack, a fake user can create massive amount of fake identities towards the target OSNs for unfairly increasing their influence. These Sybils performs the distribution of malwares, spams, bad products reviews and private data collection. Recently, there exist different schemes for detect and prevent the challenging Sybil attacks. This paper review some of those works that leverages the social graph structure and make a comparative study to identify their relevance in detection of Sybil identity in OSNs.

Keywords: Online Social Network, Sybil attack, Sybil detection


  1. H. Yu, M. Kaminsky, P. B. Gibbons, and A. Flaxman , Sybilguard: Defending against Sybil attacks via social networks, IEEE/ACM Transactions On Networking, Vol. 16, No. 3, June 2008.
  2. H. Yu, P. B. Gibbons, M. Kaminsky, and F. Xiao, Sybillimit: A near-optimal social network defense against sybil attacks. IEEE/ACM Transactions On Networking, June 2010.
  3. W. Wei, F. Xu, C. C. Tan, and Q. Li, Sybildefender: Defend against sybil attacks in large social networks, IEEE Transactions On Parallel and Distributed Systems, 2013.
  4. J. Xue, Z. Yang, X. Yang, X. Wang, L. Chen, and Y. Dai,Votetrust: Leveraging friend invitation graph to defend against social network sybils, IEEE Transaction, June 2016.
  5. G. Danezis and P. Mittal, SybilInfer: Detecting sybil nodes using social networks,in Proc. Netw. Distrib. Syst. Security, 2009.
  6. Q. Cao, M. Sirivianos, X. Yang, and T. Pregueiro, Aiding the detection of fake accounts in large scale social online services, in Proc. 9th USENIX Conf. Netw. Syst. Des. Implementation, p. 15, 2012
  7. N. Tran, B. Min, J. Li, and L. Subramanian, Sybil resilient online content voting, in Proc. of NSDI, 2009
  8. Y. Boshmaf, D. Logothetisy, G. Siganosz, J. L. Jorge Lerax, M. Ripeanu, and K. Beznosov, Integro: Leveraging victim prediction for robust fake account detection in osns, in Proc. of NDSS, 2015.
  9. Dieudonne Mulamba, Indrajit Ray, and Indrakshi Ray, SybilRadar: A Graph-Structure Based Framework for Sybil Detection in Online Social Networks,IFIP Advances in Information and Communication Technology, vol 471. Springer,2016.
  10. Qiang Cao Xiaowei Yang,SybilFence: Improving Social-Graph-Based Sybil Defenses with User Negative Feedback, WOSN 2012 on March 14.