Game-Theoretic Resilience Framework for Cyber-Physical Microgrids using Multi-Agent Reinforcement Learning

The increasing reliance on cyber physical infrastructure in modern power systems has amplified the risk of targeted cyber attacks, necessitating robust and adaptive resilience strategies. This paper presents a mathematically rigorous game theoretic framework to evaluate and enhance microgrid resilience using a combination of quantitative resilience metrics Load Served Ratio LSR, Critical Load Resilience CLR, Topological Survivability Score TSS, and DER Resilience Score DRS. These are integrated into a unified payoff matrix using the Analytic Hierarchy Process AHP to assess attack defense interactions. The framework is formalized as a finite horizon Markov Decision Process MDP with formal convergence guarantees and computational complexity bounds. Three case studies are developed 1. static attacks analyzed via Nash equilibrium, 2. severe attacks incorporating high impact strategies, and 3. adaptive attacks using Stackelberg games, regret matching, softmax heuristics, and Multi Agent Q Learning. Rigorous theoretical analysis provides convergence proofs with explicit rates , PAC learning sample complexity bounds, and computational complexity analysis. The framework is tested on an enhanced IEEE 33bus distribution system with DERs and control switches, demonstrating the effectiveness of adaptive and strategic defenses in improving cyber physical resilience with statistically significant improvements of 18.7% 2.1% over static approaches.

Methodology for Detecting Energy Anomalies due to Multi-Replay Attacks on Electric Vehicle Charging Infrastructure

The increasing deployment of Electric Vehicle Charging Infrastructures (EVCIs) introduces cybersecurity challenges, particularly due to inherent vulnerabilities, making them susceptible to cyberattacks. The vulnerable points in EVCI are charging ports, which serve as the links between the EVs and the EVCI as they transfer the data along with the power. Data spoofing attacks targeting these ports can compromise security, reliability, and overall system performance by introducing anomalies in operational data. An efficient method for identifying the charging port current magnitude variations is presented in this research. The MATLAB/SIMULINK environment simulates an EVCI system for various data generating scenarios. A Temporal Convolution Network - Autoencoder (TCN-AE) model is used in training the multivariate time series data of EVCI and reconstructing it. The abnormalities in data are that various charging port current magnitudes are replaced with their respective data of different durations, thus enabling the replay attack scenarios. To detect anomalies, the error between the original and reconstructed data is computed, and these error values are used for detecting the anomalies. With the help of the mean vector and covariance matrices of the errors, the anomaly score is computed in the form of Mahalanobis distance. The threshold is obtained from the short sub-sequence of the errors and optimized for the whole time series data. The obtained optimal threshold is compared with the anomaly score to detect the anomaly. The model demonstrates robust performance in data reconstruction by identifying anomalies with an accuracy of 99.64%, to enhance the reliability and security in operations of EVCI.