Securing an Electric Vehicle Using a Predictive Model

This study presents an efficient approach to enhance the security of electric vehicles (EVs) using interpretable machine learning (IML). Each EV communication activity is a combination of patterns generated by various control systems and data exchanges. Threat detection faces challenges because these patterns evolve with time, and they are described by specific statistical and behavioral features. These features serve as the inputs for machine learning algorithms. Multiple classifiers are proposed for detecting cyberattacks in EV environments, and their performance is improved through advanced tuning techniques. To ensure transparency in decision-making, interpretability methods are employed to explain feature contributions and detection outcomes. The integration of interpretability highlights the most influential features driving classification, thereby improving trust, usability, and applicability of the proposed framework in real-world EV cybersecurity.