Work

On Enhancing Intersection Applications With Misbehavior Detection and Mitigation

Year: 2024

Type: conference-paper

Abstract: 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.

Source: IEEE Vehicular Technology Conference

DOI: 10.1109/vtc2024-fall63153.2024.10757840

Authors: Jiahao Zhang, Ziyi Liu, Ines Ben Jemaa, Francesca Bassi, Fawzi Nashashibi

Institutions: Institut de Recherche Technologique SystemX, Institut national de recherche en sciences et technologies du numérique

Language: English

Topic: Vehicular Ad Hoc Networks (VANETs)

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Sustainable cities and communities

Open Access status: green

Funder: European Commission