GENERATIVE ADVERSARIAL NETWORK–BASED INTRUSION DETECTION FOR SECURING INVEHICLE COMMUNICATION IN ELECTRIC VEHICLES

The increasing connectivity of in-vehicle electronic control systems has intensified the need for robust cyber security solutions, especially for the Controller Area Network (CAN) bus. This study proposes a deep learning–based Intrusion Detection System (IDS) utilizing Generative Adversarial Network (GAN) architecture to detect anomalous CAN bus traffic in real time. The GAN model is trained solely on legitimate CAN messages, enabling it to learn the underlying statistical patterns of normal communication without relying on predefined attack signatures. The proposed GAN-IDS demonstrate strong detection performance, achieving an accuracy of 98.7% and an F1-Score of 98.5%, outperforming conventional deep learning baselines. To assess deployment feasibility, the discriminator is optimized using Tensor Flow Lite (TFLite) and deployed on a Raspberry Pi 4 integrated with a PiCAN2 interface. Hardware evaluation confirms real-time operation with a low detection latency of 2.9 milliseconds per message sequence. System interpretability is further enhanced through SHapley Additive exPlanations (SHAP), which identify CAN ID, engine torque, and RPM as the most influential features contributing to anomaly classification. The proposed GAN-based IDS offer a scalable, manufacturer-independent, and non-intrusive cyber security solution for modern Electric Vehicles. Its combination of high detection performance, real-time hardware deployment, and interpretable decision-making marks a significant step toward more intelligent and resilient security mechanisms for future connected and autonomous vehicles.