基于行為特征分析的社交網(wǎng)絡(luò)女巫節(jié)點檢測機(jī)制

吳大鵬; 司書山; 閆俊杰; 王汝言

doi:10.11999/JEIT170246

基于行為特征分析的社交網(wǎng)絡(luò)女巫節(jié)點檢測機(jī)制

doi: 10.11999/JEIT170246

基金項目:

國家自然科學(xué)基金(61371097)，重慶高校創(chuàng)新團(tuán)隊建設(shè)計劃(CXTDX201601020)

計量
- 文章訪問數(shù): 1216
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- 被引次數(shù): 0
出版歷程
- 收稿日期: 2017-03-29
- 修回日期: 2017-07-20
- 刊出日期: 2017-09-19

Behaviors Analysis Based Sybil Detection in Social Networks

Funds:

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

摘要

摘要: 通過制造大量非法虛假身份，女巫攻擊者可以提高自身在社交網(wǎng)絡(luò)中的影響力，影響網(wǎng)絡(luò)中社交個體中繼選擇意愿，竊取社交個體隱私，對其利益造成嚴(yán)重威脅。在對女巫節(jié)點行為特征分析的基礎(chǔ)上，該文提出一種適用于社交網(wǎng)絡(luò)的女巫節(jié)點檢測機(jī)制，通過節(jié)點間靜態(tài)相似度和動態(tài)相似度評估節(jié)點影響力，并篩選可疑節(jié)點，進(jìn)而觀察可疑節(jié)點的異常行為，利用隱形馬爾科夫模型推測女巫節(jié)點通過偽裝所隱藏的真實身份，更加精確地檢測女巫節(jié)點。分析結(jié)果表明，所提機(jī)制能有效提高女巫節(jié)點的識別率，降低誤檢率，更好地保護(hù)社交個體的隱私和利益。
- 社交網(wǎng)絡(luò) /
- 女巫節(jié)點檢測 /
- 行為特征 /
- 隱形馬爾科夫模型
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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