An Adaptive Cyber-Resilient Framework for P2P Energy Trading under Optimal Stealthy Attacks

Abstract

The increasing adoption of peer to peer energy trading has enabled prosumers to actively participate in decentralized energy markets. However, the growing dependence of P2P energy markets on communication infrastructures and distributed coordination mechanisms introduces an expanded cyberattack surface, enabling sophisticated adversaries to manipulate market interactions while remaining concealed from conventional detection mechanisms. This paper first designs an optimal stealthy attack that maximizes economic disruption of the MicroGrids, while preserving the apparent convergence characteristics of the ADMM based market-clearing process. To detect such attacks, a Mahalanobis Boosting detector is developed using market layer features to effectively capture inter feature dependencies and distinguish compromised updates from normal market operation. An adaptive rho based mitigation strategy is then introduced to limit the propagation of compromised information and restore robust market operation once an attack is flagged. The effectiveness of the proposed framework is demonstrated through comprehensive case studies, achieving an attack detection accuracy of 98.49% while the proposed mitigation strategy reduces the operating cost by approximately 18.43% compared to the unmitigated attack case. The proposed approach provides a practical pathway toward trustworthy and cyber-secure distributed P2P energy trading systems.