Anomaly Detection of Controllable Electric Vehicles through Node Equation against Aggregation Attack

The rapid proliferation of electric vehicle (EV) charging infrastructure introduces critical cybersecurity vulnerabilities to power grids system. This study presents an innovative anomaly detection framework for EV charging stations, addressing the unique challenges posed by third-party aggregation platforms. Our approach integrates node equations-based on the parameter identification with a novel deep learning model, xDeepCIN, to detect abnormal data reporting indicative of aggregation attacks. We employ a graph-theoretic approach to model EV charging networks and utilize Markov Chain Monte Carlo techniques for accurate parameter estimation. The xDeepCIN model, incorporating a Compressed Interaction Network, has the ability to capture complex feature interactions in sparse, high-dimensional charging data. Experimental results on both proprietary and public datasets demonstrate significant improvements in anomaly detection performance, with F1-scores increasing by up to 32.3% for specific anomaly types compared to traditional methods, such as wide & deep and DeepFM (Factorization-Machine). Our framework exhibits robust scalability, effectively handling networks ranging from 8 to 85 charging points. Furthermore, we achieve real-time monitoring capabilities, with parameter identification completing within seconds for networks up to 1000 nodes. This research contributes to enhancing the security and reliability of renewable energy systems against evolving cyber threats, offering a comprehensive solution for safeguarding the rapidly expanding EV charging infrastructure.