Review on Security Information of Electric Vehicles (EVs) using Artificial Intelligence and Machine Learning (AIML)

With the rapid integration of Electric Vehicles (EVs) into modern transportation systems, ensuring their security against potential threats has become paramount. This review paper comprehensively explores the utilization of Artificial Intelligence (AI) and Machine Learning (ML) techniques to fortify the security of EVs. The amalgamation of AI and ML not only promises enhanced security protocols but also facilitates intelligent decision-making in real-time scenarios. The paper begins by delineating the inherent vulnerabilities of EVs, ranging from communication networks to onboard systems, which expose them to diverse cyber threats. Subsequently, it delves into the application of AI and ML algorithms for threat detection, anomaly identification, and predictive maintenance in EVs. These techniques leverage advanced data analytics to discern patterns and anomalies, thereby fortifying the EV's security posture. Furthermore, the review elucidates the role of AI-driven intrusion detection systems (IDS) and anomaly detection algorithms in preempting cyber-attacks on EVs. It also investigates the potential of reinforcement learning algorithms in adapting security measures dynamically based on evolving threats. Moreover, the paper discusses the integration of AI- powered authentication mechanisms to safeguard EVs against unauthorized access and malicious interventions. In addition to cyber threats, the review addresses physical security concerns by examining AI-enabled surveillance systems and autonomous security mechanisms for EV charging stations and parking facilities. Furthermore, it assesses the ethical implications and privacy concerns associated with the deployment of AI-driven security solutions in the EV ecosystem. By synthesizing insights from diverse scholarly works and Empirical studies, this review paper provides a comprehensive understanding of the evolving landscape of AI and ML-based security measures for Electric Vehicles. It not only underscores the significance of proactive security measures but also elucidates the challenges and future research directionsin leveraging AIto bolster the security of EVs in an increasingly interconnected and digitized transportation environment.