| Title | Malicious attack detection based on traffic-flow information fusion |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Chen, Ye, Lai, Yingxu, Zhang, Zhaoyi, Li, Hanmei, Wang, Yuhang |
| Conference Name | 2022 IFIP Networking Conference (IFIP Networking) |
| Date Published | jun |
| Keywords | attack detection, composability, Detectors, feature extraction, information fusion, machine learning algorithms, Metrics, Observers, performance evaluation, pubcrawl, Resiliency, Roads, smart transportation, sybil attacks, Traffic flow characterization, Vehicular Networks |
| Abstract | While vehicle-to-everything communication technology enables information sharing and cooperative control for vehicles, it also poses a significant threat to the vehicles' driving security owing to cyber-attacks. In particular, Sybil malicious attacks hidden in the vehicle broadcast information flow are challenging to detect, thereby becoming an urgent issue requiring attention. Several researchers have considered this problem and proposed different detection schemes. However, the detection performance of existing schemes based on plausibility checks and neighboring observers is affected by the traffic and attacker densities. In this study, we propose a malicious attack detection scheme based on traffic-flow information fusion, which enables the detection of Sybil attacks without neighboring observer nodes. Our solution is based on the basic safety message, which is broadcast by vehicles periodically. It first constructs the basic features of traffic flow to reflect the traffic state, subsequently fuses it with the road detector information to add the road fusion features, and then classifies them using machine learning algorithms to identify malicious attacks. The experimental results demonstrate that our scheme achieves the detection of Sybil attacks with an accuracy greater than 90 % at different traffic and attacker densities. Our solutions provide security for achieving a usable vehicle communication network. |
| DOI | 10.23919/IFIPNetworking55013.2022.9829793 |
| Citation Key | chen_malicious_2022 |
Malicious attack detection based on traffic-flow information fusion