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| 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) |
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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.
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Received: 29 March 2017
Published: 14 August 2017
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| Fund:The National Natural Science Foundation of China (61371097), The Program for Innovation Team Building at Institutions of Higher Education in Chongqing (CXTDX2016 01020) |
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Corresponding Authors:
SI Shushan
E-mail: 1063603919@qq.com
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