The rapid deployment of electric vehicle charging stations (EVCS) within distribution networks requires intelligent, adaptive control to maintain the grid's resilience and reliability. In this work, we propose PHANTOM, a physics-aware adversarial network through training and optimization of multi-agent reinforcement learning model. PHANTOM integrates a physics-informed neural network (PINN) enabled by federated learning (FL) that functions as a digital twin of EVCS-integrated systems, ensuring physically consistent modeling of operational dynamics and constraints. Building on this digital twin, we construct a multi-agent RL environment that uses deep Q-networks (DQN) and soft actor-critic (SAC) methods to develop adversarial false data injection (FDI) strategies that can bypass conventional detection mechanisms. To examine the broader grid-level consequences, a transmission-distribution (T&D) dual simulation platform is developed, allowing us to capture cascading interactions between EVCS disturbances at the distribution level and the operations of the bulk transmission system. Results demonstrate how learned attack policies disrupt load balancing and induce voltage instabilities that propagate across T&D boundaries. These findings highlight the critical need for physics-aware cybersecurity to ensure the resilience of large-scale vehicle-grid integration.

Incorporating advanced information and communication technologies enhances smart grid (SG) operation, while increasing vulnerability to false data injection (FDI) attacks. Identifying and characterizing FDI attack vectors is crucial, as they can jeopardize SG system stability and protection. State-of-the-art (SOTA) attack analytics predominantly employ machine learning (ML) to extract attack vectors that can evade rules-based bad-data detectors. While scalable, these approaches offer no guarantees of identification or stealth and often yield simplistic attack vectors detectable by ML-based anomaly detection models (ADMs). Formal methods, in contrast, can synthesize verifiable attack vectors while ignoring ML-based ADM. Several tools in other domains attempt to identify attack vectors against ML-based ADMs; however, they apply to systems with straightforward control dynamics and cannot be directly transferred to complex, interdependent SG control systems. To address these gaps, we introduce MISGUIDE, a defense-aware attack analytics that jointly models LFC dynamics and an ML-based ADM to extract verifiable, multi-timeslot FDI attack vectors that can trip under/over-frequency relays while remaining stealthy. The ADM used in MISGUIDE can detect 100% of the attack vectors found by SOTA attack analytics. Using real-world load data, we validate the attack vectors generated by MISGUIDE through hardware-in-the-loop OPAL-RT simulations on the IEEE 39-bus system.

This work introduces a novel physics-informed neural network (PINN)-based framework for modeling and optimizing false data injection (FDI) attacks on electric vehicle charging station (EVCS) networks, with a focus on centralized charging management system (CMS). By embedding the governing physical laws as constraints within the neural network’s loss function, the proposed framework enables scalable, real-time analysis of cyber-physical vulnerabilities. The PINN models EVCS dynamics under both normal and adversarial conditions while optimizing stealthy attack vectors that exploit voltage and current regulation. Evaluations on the IEEE 33-bus system demonstrate the framework’s capability to uncover critical vulnerabilities. These findings underscore the urgent need for enhanced resilience strategies in EVCS networks to mitigate emerging cyber threats targeting the power grid. Furthermore, the framework lays the groundwork for exploring a broader range of cyber-physical attack scenarios on EVCS networks, offering potential insights into their impact on power grid operations. It provides a flexible platform for studying the interplay between physical constraints and adversarial manipulations, enhancing our understanding of EVCS vulnerabilities. This approach opens avenues for future research into robust mitigation strategies and resilient design principles tailored to the evolving cybersecurity challenges in smart grid systems.