Conference paper Open Access
Reinforcement Learning-based Misbehaviour Detection in V2X Scenarios
Sedar, Roshan; Kalalas, Charalampos; Vázquez-Gallego, Francisco; Alonso-Zárate, Jesús
Emerging vehicle-to-everything (V2X) services rely on the secure exchange of periodic messages between vehicles and between vehicles and infrastructure. However, transmission of false/incorrect data by malicious vehicles may pose important security perils. Therefore, it is essential to detect safety-threatening erroneous information and mitigate potentially detrimental effects on road users. In this paper, we assess the effectiveness of a reinforcement learning (RL) approach for misbehaviour detection in V2X scenarios using an open-source dataset. Considering the case of sudden-stop attacks, the performance of RL-based detection is evaluated over commonly used detection metrics. Our research outcomes reveal that misbehaving vehicles can be accurately detected by exploiting real-time position and speed patterns.
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- Publication date:
- September 7, 2021
- DOI:
-
- Keyword(s):
- V2X Misbehaviour Detection Reinforcement Learning
- Grants:
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European Commission:
- INSPIRE-5Gplus - INtelligent Security and PervasIve tRust for 5G and Beyond (871808)
- Meeting:
- IEEE International Mediterranean Conference on Communications and Networking 2021 (IEEE MeditCom 2021), virtual event, 7-10 September 2021
- License (for files):
- Creative Commons Attribution 4.0 International