AI-Driven Intrusion Detection Systems (IDS) for Securing V2V and V2X Communications

Abstract

The rapid evolution of vehicular communication technologies, such as Vehicle-to-Vehicle (V2V) and Vehicle-to-Everything (V2X), necessitates robust security mechanisms to protect against sophisticated cyberattacks. This paper explores the use of Artificial Intelligence (AI) and Machine Learning (ML) algorithms to enhance Intrusion Detection Systems (IDS) for vehicular networks. A hybrid approach combining deep learning and traditional IDS techniques is proposed to handle real-time, high-throughput traffic. Specific attack vectors like Sybil attacks and False Data Injection (FDI) are analysed, and novel detection strategies are outlined. The proposed framework demonstrates superior performance in detecting anomalies and mitigating unauthorized access, paving the way for secure and reliable vehicular communication systems.