Trust Management Framework for Misbehavior Detection in Collective Perception Services

Jiahao Zhang (1) , Ines Ben-Jemaa (1) , Fawzi Nashashibi (2)

1 IRT SystemX
2 ASTRA - Systèmes de transport automatisés et sécurisés

Collective Perception Messages (CPM) enable vehicles to share their perceived objects with their neighbors in V2X network. These perception data extend local vehicles’ perception and consequently improve road safety awareness. However, attacks on perception data are challenging and require advanced and efficient misbehavior detection mechanism especially in specific road scenarios where contradictory information need to be analysed. In this work, we introduce a trust management framework to detect misbehaving nodes through transmitted CPM messages. Our framework is based on trust assessment built through several processing steps. It addresses conflict situation when contradictory data are received using the Subjective Logic mechanism. The results show that our solution is effective in detecting misbehaving nodes based on their attributed trust scores. In addition, we show the impact of our solution and some CPM configuration parameters on safety services and especially on risk anticipation in intersection scenarios.

Domaines: Cryptographie et sécurité [cs.CR]

Jiahao Zhang, Ines Ben-Jemaa, Fawzi Nashashibi. Trust Management Framework for Misbehavior Detection in Collective Perception Services. ICARCV 2022 - 17th International Conference on Control, Automation, Robotics and Vision, Dec 2022, Singapore, Singapore. ⟨hal-03792577⟩