Annual Computer Security Applications Conference (ACSAC) 2023

Detection of Anomalies in Electric Vehicle Charging Sessions

Electric Vehicle (EV) charging involves a complex system with cyber-physical components, backend systems, and communication protocols. A potential security incident in this system can open up cyber-physical threats and, for instance, lead to EV battery fires or power grid blackouts. In this paper, we propose a hybrid Intrusion Detection System (IDS) method consisting of regression-based charging session forecasting and anomaly detection. The method considers an EV's detailed charging behavior throughout a session and we discuss and evaluate different design choices. For anomaly detection, we consider both classification- and novelty-based models as well as an ensemble method to combine both models. We perform evaluations based on real-world EV charging session data with simulated attacks. Our results show that regression-based forecasting provides a significant increase in detection performance for attacks affecting individual reports during a charging session. Additionally, the proposed ensemble method, which combines artificial neural network-based classification and local outlier factor-based novelty detection, can maintain a low false alarm rate while offering good detection performance wrt. known attacks as well as generalization to previously unseen attacks. We thus argue that the proposed solution can provide a positive contribution to EV charging security, resilience, and trustworthiness.

Dustin Kern
Darmstadt University of Applied Sciences

Christoph Krauß
Darmstadt University of Applied Sciences

Matthias Hollick
Technical University of Darmstadt

Paper (ACM DL)