Behaviors Analysis Based Sybil Detection in Social Networks
WU Dapeng SI Shushan YAN Junjie WANG Ruyan
(School of Telecommunication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China)
(Optical Communication and Network Key Laboratory of Chongqing, Chongqing 400065, China)
Abstract：Sybil attackers can improve their own influence in social networks by creating a large number of illegal illusive identities then affect the social individuals’ choice of relays and steal individuals’ privacy, which seriously threatens the interests of social individuals. Based on the analysis of the Sybil’s behaviors, a Sybil detection mechanism applied to social networks is proposed in this paper. The influence of nodes is calculated according to static similarity and dynamic similarity and then selecting the suspicious nodes based on the influence. Next, using the Hidden Markov Model (HMM) to infer the true identity of suspicious nodes by observing their abnormal behaviors, thus detecting the Sybil more precisely. Analysis results show that the proposed mechanism can effectively improve the recognition rate and reduce the false detection rate of the Sybil and thereby protecting the privacy and interests of social individuals better.
吴大鹏,司书山,闫俊杰,王汝言. 基于行为特征分析的社交网络女巫节点检测机制[J]. 电子与信息学报, 2017, 39(9): 2089-2096.
WU Dapeng, SI Shushan, YAN Junjie, WANG Ruyan. Behaviors Analysis Based Sybil Detection in Social Networks. JEIT, 2017, 39(9): 2089-2096.
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