SybilFuse: Combining Local Attributes with Global Structure to Perform Robust Sybil Detection-猫眼课题宝

DOI：

10.1109/cns.2018.8433147

发表时间：

2018-03

期刊：

ArXiv

影响因子：

通讯作者：

中科院分区：

其他

文献类型：

作者：

Peng Gao;Binghui Wang;N. Gong;Sanjeev R. Kulkarni;Kurt Thomas;Prateek Mittal

Sybil攻击正变得越来越普遍，并对在线社交系统构成重大威胁;单个攻击者可以在系统中注入多个合谋身份，从而危及安全和隐私。最近的作品利用基于社交网络的信任关系来抵御Sybil攻击。然而，现有的防御基于对网络结构的过于简单化的假设，这在现实世界的社交网络中不一定成立。认识到这些局限性，我们提出Sybilestrium，一个深入的防御框架，当过于简化的假设放松时，Sybil检测。Sybillet采用集体分类方法，首先训练局部分类器来计算节点和边缘的局部信任分数，然后通过加权随机游走和循环信念传播机制将局部分数传播到全局网络结构中。我们评估了我们的框架在合成和现实世界的网络拓扑结构，包括一个大规模的，标记的Twitter网络，包括20 M节点和265 M边缘，并证明Sybilocide优于国家的最先进的方法显着。特别是，Sybilecom在顶级节点中实现了98%的Sybil覆盖率。

Sybil attacks are becoming increasingly widespread and pose a significant threat to online social systems; a single adversary can inject multiple colluding identities in the system to compromise security and privacy. Recent works have leveraged social network-based trust relationships to defend against Sybil attacks. However, existing defenses are based on oversimplified assumptions about network structure, which do not necessarily hold in real-world social networks. Recognizing these limitations, we propose SybilFuse, a defense-in-depth framework for Sybil detection when the oversimplified assumptions are relaxed. SybilFuse adopts a collective classification approach by first training local classifiers to compute local trust scores for nodes and edges, and then propagating the local scores through the global network structure via weighted random walk and loopy belief propagation mechanisms. We evaluate our framework on both synthetic and real-world network topologies, including a large-scale, labeled Twitter network comprising 20M nodes and 265M edges, and demonstrate that SybilFuse outperforms state-of-the-art approaches significantly. In particular, SybilFuse achieves 98% of Sybil coverage among top-ranked nodes.

SybilFuse: Combining Local Attributes with Global Structure to Perform Robust Sybil Detection

文献摘要

被引文献

相似文献