| Title | Credibility Enhanced Temporal Graph Convolutional Network Based Sybil Attack Detection On Edge Computing Servers |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Luo, Baiting, Liu, Xiangguo, Zhu, Qi |
| Conference Name | 2021 IEEE Intelligent Vehicles Symposium (IV) |
| Date Published | jul |
| Keywords | composability, Error analysis, Metrics, privacy, pubcrawl, Real-time Systems, resilience, Resiliency, Servers, simulation, sybil attacks, System performance, vehicular ad hoc networks |
| Abstract | The emerging vehicular edge computing (VEC) technology has the potential to bring revolutionary development to vehicular ad hoc network (VANET). However, the edge computing servers (ECSs) are subjected to a variety of security threats. One of the most dangerous types of security attacks is the Sybil attack, which can create fabricated virtual vehicles (called Sybil vehicles) to significantly overload ECSs' limited computation resources and thus disrupt legitimate vehicles' edge computing applications. In this paper, we present a novel Sybil attack detection system on ECSs that is based on the design of a credibility enhanced temporal graph convolutional network. Our approach can identify the malicious vehicles in a dynamic traffic environment while preserving the legitimate vehicles' privacy, particularly their local position information. We evaluate our proposed approach in the SUMO simulator. The results demonstrate that our proposed detection system can accurately identify most Sybil vehicles while maintaining a low error rate. |
| DOI | 10.1109/IV48863.2021.9575361 |
| Citation Key | luo_credibility_2021 |
Credibility Enhanced Temporal Graph Convolutional Network Based Sybil Attack Detection On Edge Computing Servers