Behaviors Analysis Based Sybil Detection in Social Networks

WU Dapeng; SI Shushan; YAN Junjie; WANG Ruyan

doi:10.11999/JEIT170246

Volume 39 Issue 9

Sep. 2017

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Article Navigation > Journal of Electronics & Information Technology > 2017 > 39(9): 2089-2096

WU Dapeng, SI Shushan, YAN Junjie, WANG Ruyan. Behaviors Analysis Based Sybil Detection in Social Networks[J]. Journal of Electronics & Information Technology, 2017, 39(9): 2089-2096. doi: 10.11999/JEIT170246

Citation:

WU Dapeng, SI Shushan, YAN Junjie, WANG Ruyan. Behaviors Analysis Based Sybil Detection in Social Networks[J]. Journal of Electronics & Information Technology, 2017, 39(9): 2089-2096. doi: 10.11999/JEIT170246

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Behaviors Analysis Based Sybil Detection in Social Networks

doi: 10.11999/JEIT170246

Funds:

The National Natural Science Foundation of China (61371097), The Program for Innovation Team Building at Institutions of Higher Education in Chongqing (CXTDX2016 01020)

Received Date: 2017-03-29
Rev Recd Date: 2017-07-20
Publish Date: 2017-09-19

Abstract

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 Sybils 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.
- Social networks,
- Sybil detection,
- Behavior characteristics,
- Hidden Markov Model (HMM)

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References(22)

References

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