期刊
出版社
IEEE
DOI: 10.1109/MeditCom49071.2021.9647514
关键词
V2X; Misbehaviour Detection; Reinforcement Learning
资金
- H2020-INSPIRE5Gplus project [871808]
- Spanish MINECO project SPOT5G [TEC2017-87456-P]
- Generalitat de Catalunya [2017 SGR 891]
This research assesses the effectiveness of a reinforcement learning approach for detecting misbehaviour in V2X scenarios, showing that misbehaving vehicles can be accurately detected by exploiting real-time position and speed patterns.
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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