ISSN 1000-1239 CN 11-1777/TP

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社会化网络服务中的信任扩张与控制

康乐 荆继武 王跃武   

  1. (信息安全国家重点实验室(中国科学院研究生院) 北京 100049) (kangle@is.ac.cn)
  • 出版日期: 2010-09-15

The Trust Expansion and Control in Social Network Service

Kang Le, Jing Jiwu, and Wang Yuewu   

  1. (State Key Laboratory of Information Security (Graduate University of Chinese Academy of Sciences), Beijing 100049)
  • Online: 2010-09-15

摘要: 社会化网络服务(SNS)是近年来兴起的一类网络应用.随着该类应用的不断深入和发展,其所面临的安全威胁不断增加.其中,恶意信任扩张是在SNS中实施攻击的重要前提.因此了解SNS信任网络的恶意扩张的特性及其与正常信任扩张的区别对于保障SNS网络安全具有重要意义.通过分析社会化网络服务中的虚拟网络结构及其形成过程的特点,提出社会化网络动态模型(SNDM),并利用该模型分析SNS网络中的恶意信任扩张行为.在此基础上,提出了以信任评估为基础的恶意信任扩张控制策略,并通过仿真实验对该策略的有效性进行了分析.实验结果显示,该策略可以有效地限制SNS网络中的恶意信任扩张.

关键词: 社会化网络服务, 信任, 恶意信任扩张, 信任控制, 社会化网络动态模型

Abstract: Social network service(SNS) is a new emerging Web application form. With the growth of SNS in application, the trust that plays the role of connecting people brings both good user experience and threats. Trust expansion is not only the means that SNS users construct their online social network with, but also exploited by the attackers to collect victims. Hence, it is desirable to detect the malicious trust expansion behaviors to prevent subsequent attacks. By analyzing the forming process of SNS complex network via a growth model (SNDM), it is discovered that the malicious users are quite possible to adopt some measures to avoid being exposed. This will bring in unavoidable difference in behavior features, so the difference is a weak point that can be exploited to identify the malicious users. In this paper the detailed analysis about the above issue is given, and a practical evaluation-based filter is designed to detect the attackers. Based on the filter a resilient trust control strategy is proposed to restrict and weaken the malicious users, and the normal users will not be bothered. The analysis and conclusions are positively supported by simulation or experiment in a real SNS scenario.

Key words: social network service, trust, malicious trust expansion, trust control, social network dynamic model (SNDM)