Stealthy Shields: Adversarial DRL Intelligence for Securing EV Charging Communication Systems

The widespread adoption of electric vehicles (EVs) has led to a significant increase in the deployment of charging infrastructures, making them critical components of modern power grids. However, integrating advanced communication technologies into these infrastructures has also enabled sophisticated cyber threats. Although existing research has proposed various solutions, many approaches do not sufficiently address the challenges posed by advanced attack methodologies powered by deep reinforcement learning (DRL), which can adapt their strategies over time, coordinate actions across multiple EVs, and remain stealthy under protocol-compliant constraints. In this paper, we present a novel Multi-Layer Adversarial DRL (MLADRL) framework designed to tackle these challenges. Our approach first utilizes DRL to develop more destructive and coordinated attack vectors, where multiple EVs launch attacks, going beyond the single-EV attacks typically seen in the literature. We combine these DRL-generated attack samples with benign samples and handcrafted attacks to build robust datasets. Based on these datasets, we then design a DRL-based Intrusion Detection System (IDS) specifically designed to detect these advanced cyber threats. We evaluate the effectiveness of our framework across multiple attack scenarios, including varying proportions of malicious EVs and different levels of attack complexity. The results demonstrate that our MLADRL framework significantly outperforms traditional detection methods, maintaining high detection accuracy even against more destructive and complex attack strategies. The proposed DRL-based IDS, trained on the generated adversarial datasets, achieves remarkable performance. Compared to baseline methods, our DRL-based IDS attains 99.4% and 99.9% accuracy, 99.4% and 99.9% precision, and 99.6% and 99.9% recall (F1-scores: 99.6% and 99.9%) for LSTM-based and Transformer-based models, respectively.