防御定向社交网络中的sybil攻击

Pengfei Liu, Xiaohan Wang, Xiangqian Che, Zhaoqun Chen, Yuantao Gu
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引用次数: 8

摘要

在本文中,我们试图解决定向社交网络中防御sybil攻击的问题。我们提出了一套衡量网络分区质量的方法,并以模块化为特例。提出了一种基于测度集和迭代优化的符号区域检测算法。该算法使用现实世界社会拓扑的一个子集进行评估,并被证实是有效的解决问题。此外,将该算法与SybilDefender算法进行了比较,结果表明该算法在有向社交网络中的sybilregion检测问题上具有优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Defense against sybil attacks in directed social networks
In this paper, we attempt to solve the problem of defense against sybil attacks in directed social networks. We propose a set of measures for the quality of network partitions, with modularity as a special case. We present an algorithm based on the set of measures and iterative optimization to detect the sybil region. The algorithm is evaluated using a subset of real-world social topology and is confirmed to be efficient for solving the problem. Moreover, a comparison between the proposed algorithm and SybilDefender is provided, which shows that the proposed algorithm is superior for the sybil region detection problem in directed social networks.
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