Bidirectional GNN-Based Intrusion Detection of Malware Injection Attacks in EV Charging Stations

The growing popularity of electric vehicles (EVs) has rendered public EV charging stations (EVCSs) vital for alleviating range anxiety and supporting long-distance travel. However, recent studies reveal security vulnerabilities in EVs and EVCSs against attacks. This paper addresses these security concerns by introducing injection attacks on the front-end Vehicle-to-Grid (V2G) communication using the ISO 15118 protocol. Malicious EV owners or compromised EVCS supply equipment can inject harmful packets, potentially leading to runtime modifications and malware attacks. To counter this threat, we propose an innovative bidirectional recurrent attentive graph neural network (BiRAGNN)-based intrusion detection system (IDS) that dynamically captures spatiotemporal aspects and the bidirectional flow of information, while leveraging an attention mechanism to effectively detect injection attacks within EVs and EVCSs. The BiRAGNN model is founded on a probabilistic charging graph of real cities. Other deep learning and graph-based IDSs are also investigated as evaluation benchmarks. The IDSs are examined against a standalone system (from a single EVCS) and multi-node systems (from 8, 50, and 100-node EVCSs), all with packet-level, flow-level, and fused packet and flow-level data. The proposed BiRAGNN-based IDS offers a detection accuracy of 99% on the 100-node fused dataset, surpassing the benchmarks by 5-8% , offering EVs and public EVCSs resilience against cyber threats.