On Enhancing Intersection Applications With Misbehavior Detection and Mitigation

Jiahao Zhang (1, 2) , Ziyi Liu (1) , Ines Ben Jemaa (1) , Francesca Bassi (1) , Fawzi Nashashibi (2)

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

Collective Perception Services (CPS) enable communicating entities to share their perception data in the V2X communication network. Potential attacks on extended perception data affect the CPS and may consequently degrade the safety application that rely on collective perception data. In this paper, we build an architecture that allows the integration of misbehavior detection and mitigation mechanisms with the CPS. We implement the Intersection Movement Assist (IMA) application that uses the extended perception data to calculate potential collision risks in intersection areas. We define specific safety metrics and through extensive simulations in large scale scenarios, we quantify the impact of a large number of attacks and of misbehavior detection on the safety application. Our evaluation demonstrates the ability of misbehavior detection and mitigation mechanisms to filter malicious shard perception data and consequently the benefits of using such mechanisms in improving the robustness of the safety application in complex road scenarios.

Mots clés: C-ITS; Misbehavior Detection; Simulation; Collective Perception

Domaines: Informatique [cs]

Jiahao Zhang, Ziyi Liu, Ines Ben Jemaa, Francesca Bassi, Fawzi Nashashibi. On Enhancing Intersection Applications With Misbehavior Detection and Mitigation. IEEE 100th Vehicular Technology Conference (VTC Fall), Oct 2024, Washington, DC, United States. ⟨hal-04672473⟩