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Deeply Understanding Graph-Based Sybil Detection Techniques via Empirical Analysis on Graph Processing
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作者 Jian Mao Xiang Li +1 位作者 Qixiao Lin Zhenyu Guan 《China Communications》 SCIE CSCD 2020年第10期82-96,共15页
Sybil attacks are one of the most prominent security problems of trust mechanisms in a distributed network with a large number of highly dynamic and heterogeneous devices,which expose serious threat to edge computing ... Sybil attacks are one of the most prominent security problems of trust mechanisms in a distributed network with a large number of highly dynamic and heterogeneous devices,which expose serious threat to edge computing based distributed systems.Graphbased Sybil detection approaches extract social structures from target distributed systems,refine the graph via preprocessing methods and capture Sybil nodes based on the specific properties of the refined graph structure.Graph preprocessing is a critical component in such Sybil detection methods,and intuitively,the processing methods will affect the detection performance.Thoroughly understanding the dependency on the graph-processing methods is very important to develop and deploy Sybil detection approaches.In this paper,we design experiments and conduct systematic analysis on graph-based Sybil detection with respect to different graph preprocessing methods on selected network environments.The experiment results disclose the sensitivity caused by different graph transformations on accuracy and robustness of Sybil detection methods. 展开更多
关键词 Sybil attack graph preprocessing Edge computing trust model
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