Silent Threats, Smart Shields: A Dual-Strategy Framework Against Stealthy Attacks in EV Charging Systems

Smart electric vehicle charging stations (EVCSs) are crucial in advancing sustainable transportation by scheduling charging based on user preferences and grid constraints. However, their reliance on digital communication makes them vulnerable to attacks that can shift the EV aggregator's load profile to different times, leading to substantial financial losses. They can also alter charging times in ways that degrade battery health and overburden the grid. Although the existing literature has explored various strategies to mitigate these risks, most prior work has focused on simple, handcrafted charge manipulation attacks (CMAs). This makes them often fall short when confronted with artificially intelligent methods to remain undetected. To address these limitations, we propose a novel framework that both generates and defends against highly evasive CMAs. First, we utilize deep reinforcement learning (DRL) to craft advanced, stealthy attacks capable of bypassing intrusion detection systems (IDS). Second, we introduce an IDS built on LSTM variational autoencoders, which captures the nuanced temporal dependencies of smart CMAs, as well as intricate patterns. This enables our IDS to significantly enhance the detection and mitigation of complex threats. We conduct extensive simulations using real-world datasets, which reveal critical security gaps in existing benchmark approaches while highlighting the strong performance of our proposed framework. Notably, our IDS achieves detection accuracies of 0.97, 0.96, and 0.96 across different scenarios, even against highly evasive CMAs.