| Title | Deeply understanding graph-based Sybil detection techniques via empirical analysis on graph processing |
| Publication Type | Journal Article |
| Year of Publication | 2020 |
| Authors | Mao, J., Li, X., Lin, Q., Guan, Z. |
| Journal | China Communications |
| Volume | 17 |
| Pagination | 82–96 |
| ISSN | 1673-5447 |
| Keywords | aerospace industry, Attack Graphs, composability, detection performance, distributed processing, edge computing, graph preprocessing, graph processing, graph theory, graph transformations, graph-based sybil detection, heterogeneous devices, Image edge detection, Network topology, Predictive Metrics, prominent security problems, pubcrawl, refined graph structure, Resiliency, security, security of data, Sensitivity, social networking (online), social structures, Sybil attack, sybil attacks, target distributed systems, trust mechanism, trust model, Trusted Computing |
| Abstract | 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. |
| DOI | 10.23919/JCC.2020.10.006 |
| Citation Key | mao_deeply_2020 |
Deeply understanding graph-based Sybil detection techniques via empirical analysis on graph processing