Advancements in Intrusion Detection Systems for V2X: Leveraging AI and ML for Real-Time Cyber Threat Mitigation

Abstract

The proliferation of Internet of Things (IoT) technology has extended its reach to the automotive domain, notably through Vehicle-to-Everything (V2X) communication. This integration holds promise for revolutionizing road safety and efficiency by facilitating real-time data exchange between vehicles, infrastructure, pedestrians, and other entities. However, alongside these advancements come unprecedented cybersecurity challenges, necessitating the deployment of robust Intrusion Detection Systems (IDS).

This paper conducts an in-depth exploration of the current landscape of IDS tailored to the V2X environment. By examining the intricate interplay between vehicular networks and cybersecurity, we elucidate the imperative for advanced intrusion detection mechanisms.

The discussion encompasses various facets, including the nuanced design considerations imperative for effective V2X IDS deployment. It addresses the distinctive attributes of V2X communication networks, emphasizing the need for solutions capable of real-time threat detection, scalability, and adaptability to dynamic vehicular environments.

Furthermore, the paper delves into the intricate integration of artificial intelligence (AI) and machine learning (ML) techniques within IDS frameworks. Highlighting the pivotal role of AI and ML in augmenting threat prediction and mitigation capabilities, it explores methodologies for training data generation, model optimization, and real-time decision-making.

Drawing from a synthesis of contemporary research and methodologies, this article endeavors to furnish comprehensive insights into the development of advanced IDS solutions tailored for V2X networks. By amalgamating theoretical discourse with practical implications, it seeks to inform stakeholders about the evolving landscape of V2X cybersecurity and the imperative for proactive defense mechanisms in safeguarding vehicular ecosystems.