社交网络中基于结构的Sybil检测
Structure-based Sybil Detection in Social Networks via Local Rule-based Propagation
摘要：
社交网络中的Sybil检测是一个基本的安全研究问题。基于结构的方法已被证明是有前途的检测Sybils。现有的基于结构的方法可以分为基于随机游走（RW）的方法和基于循环置信传播（LBP）的方法。基于RW的方法不能同时利用标记的Sybil和标记的良性用户，这限制了它们的检测精度，和/或它们对噪声标签不鲁棒。基于LBP的方法是不可扩展的，并且不能保证收敛。在这项工作中，我们提出了SybilSCAR，一种新的基于结构的方法来检测在社交网络中的Sybils。SybilSCAR是可扩展的，收敛的，准确的，鲁棒的标签噪音。首先，我们提出了一个框架，统一基于RW和LBP的方法。在我们的框架下，这些方法可以被视为迭代地将（不同的）本地规则应用于每个用户，从而在社交图中传播标签信息。其次，我们设计了一个新的本地规则，SybilSCAR迭代地应用到每个用户检测Sybils。我们比较SybilSCAR与最先进的基于RW和基于LBP的方法理论和经验。从理论上讲，我们表明，通过适当的参数设置，SybilSCAR有一个更严格的渐近约束的数量Sybils被错误地接受到一个社会网络比现有的基于结构的方法。从经验上讲，我们使用合成Sybils的社交网络和具有真实Sybils的大规模Twitter数据集（41.7M节点和1.2B边缘）进行评估。我们的研究结果表明：1）SybilSCAR比最先进的基于RW的方法更准确，对标签噪声更鲁棒; 2）SybilSCAR比最先进的基于LBP的方法更准确，可扩展性更高一个数量级。
英文摘要：
Sybil detection in social networks is a basic security research problem. Structure-based methods have been shown to be promising at detecting Sybils. Existing structure-based methods can be classified into Random Walk (RW)-based methods and Loop Belief Propagation (LBP)-based methods. RW-based methods cannot leverage labeled Sybils and labeled benign users simultaneously, which limits their detection accuracy, and/or they are not robust to noisy labels. LBP-based methods are not scalable and cannot guarantee convergence. In this work, we propose SybilSCAR, a novel structure-based method to detect Sybils in social networks. SybilSCAR is Scalable, Convergent, Accurate, and Robust to label noise. We first propose a framework to unify RW-based and LBP-based methods. Under our framework, these methods can be viewed as iteratively applying a (different) local rule to every user, which propagates label information among a social graph. Second, we design a new local rule, which SybilSCAR iteratively applies to every user to detect Sybils. We compare SybilSCAR with state-of-the-art RW-based and LBP-based methods theoretically and empirically. Theoretically, we show that, with proper parameter settings, SybilSCAR has a tighter asymptotical bound on the number of Sybils that are falsely accepted into a social network than existing structure-based methods. Empirically, we perform evaluation using both social networks with synthesized Sybils and a large-scale Twitter dataset (41.7M nodes and 1.2B edges) with real Sybils. Our results show that 1) SybilSCAR is substantially more accurate and more robust to label noise than state-of-the-art RW-based methods; 2) SybilSCAR is more accurate and one order of magnitude more scalable than state-of-the-art LBP-based methods.
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