Defense against multi-path attacks on EV charging networks: A dependency chain analysis and DQN-BASED SOLUTION

As electric vehicle (EV) charging infrastructures evolve to become more intelligent, integrated, and interconnected, they encounter escalating cybersecurity threats. Existing studies largely emphasize single-component vulnerabilities while overlooking systemic risks arising from multi-path dependency chains. This paper conceptualizes electric vehicle charging networks as a cyber-physical system (CPS) integrated with power, communication, and control layers. To tackle the issue of multi-path attack propagation within the EV charging network, we present a risk assessment method combined with graph-based system modeling with Deep Q-Networks (DQN). The attacker’s behavior is modeled as a Markov decision process, utilizing DQN to learn optimal attack paths based on cumulative rewards. This method identifies the most vulnerable components and critical propagation pathways, facilitating the development of optimized defense strategies for the deployment of constrained security resources. Comparative experiments indicate that the proposed DQN-based defense strategy outperforms random and traditional dependency-based allocations, leading to diminished cumulative attacker rewards and enhanced network resilience through more efficient resource utilization. These findings can offer practical insights for strengthening the robustness of smart grid ecosystems against multi-stage cyberattacks.