SybilBelief：一种基于结构的Sybil检测的半监督学习方法
SybilBelief: A Semi-supervised Learning Approach for Structure-based Sybil Detection
摘要：
Sybil攻击是对分布式系统安全的根本威胁。最近，人们越来越关注利用社交网络来减轻Sybil攻击。然而，现有的方法遭受一个或多个缺点，包括仅从已知的良性或已知的Sybil节点引导，不能容忍关于已知的良性或Sybil节点的先验知识中的噪声，以及不可扩展。在这项工作中，我们的目标是克服这些缺点。为了实现这个目标，我们引入SybilBelief，一个半监督学习框架，检测Sybil节点。SybilBelief将系统中节点的社交网络、一小部分已知良性节点以及一小部分已知Sybil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。我们评估SybilBelief使用合成和现实世界的社会网络拓扑结构。我们表明，SybilBelief是能够准确地识别Sybil节点的低假阳性率和低假阴性率。SybilBelief对我们关于已知良性和Sybil节点的先验知识中的噪声具有弹性。此外，SybilBelief执行的数量级优于现有的Sybil分类机制和显着优于现有的Sybil排名机制。
英文摘要：
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 being not scalable. In this work, we aim to overcome these drawbacks. Towards 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
