SybilBelief: A Semi-Supervised Learning Approach for Structure-Based Sybil Detection
SybilBelief: A Semi-Supervised Learning Approach for Structure-Based Sybil Detection
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DOI：
10.1109/tifs.2014.2316975
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发表时间：
2014-06-01
影响因子：
6.8
通讯作者：
Mittal, Prateek
中科院分区：
文献类型：
--
作者：
Gong, Neil Zhenqiang;Frank, Mario;Mittal, Prateek
Sybil攻击是对分布式系统安全的根本威胁。最近，人们对利用社交网络来减轻Sybil攻击越来越感兴趣。然而，现有的方法存在一个或多个缺点，包括只从已知的良性或已知的Sybil节点引导，不能容忍关于已知良性或Sybil节点的先验知识中的噪声，并且不具有可扩展性。在本文中，我们的目标是克服这些缺点。为了实现这一目标，我们引入了半监督学习框架SybilBelief来检测Sybil节点。SybilBelief采用系统中节点的社会网络、一小部分已知良性节点，以及一小部分已知Sybils作为输入。然后，SybilBelief将标签信息从已知的良性和/或Sybil节点传播到系统中的其余节点。我们使用合成的和真实的社会网络拓扑来评估SybilBelief。我们证明了SybilBelief能够准确地识别具有低假阳性率和低假阴性率的Sybil节点。SybilBelief对已知良性和Sybil节点的先验知识中的噪声具有弹性。此外，SybilBelief在数量级上优于现有的Sybil分类机制，显著优于现有的Sybil排序机制。
Sybil attacks are a fundamental threat to the security of distributed systems. Recently, there has been a growing interest in leveraging social networks to mitigate Sybil attacks. However, the existing approaches suffer from one or more drawbacks, including bootstrapping from either only known benign or known Sybil nodes, failing to tolerate noise in their prior knowledge about known benign or Sybil nodes, and not being scalable. In this paper, we aim to overcome these drawbacks. Toward this goal, we introduce SybilBelief, a semi-supervised learning framework, to detect Sybil nodes. SybilBelief takes a social network of the nodes in the system, a small set of known benign nodes, and, optionally, a small set of known Sybils as input. Then, SybilBelief propagates the label information from the known benign and/or Sybil nodes to the remaining nodes in the system. We evaluate SybilBelief using both synthetic and real-world social network topologies. We show that SybilBelief is able to accurately identify Sybil nodes with low false positive rates and low false negative rates. SybilBelief is resilient to noise in our prior knowledge about known benign and Sybil nodes. Moreover, SybilBelief performs orders of magnitudes better than existing Sybil classification mechanisms and significantly better than existing Sybil ranking mechanisms.