Submodularity-Based False Data Injection Attack Strategy in DC Microgrids

Qi Liu, Chengcheng Zhao, Mengxiang Liu, Ruilong Deng, Peng Cheng

Abstract: Despite significantly enhancing system flexibility and reliability, the adoption of distributed secondary control in DC microgrids (DCmGs) introduces new vulnerabilities to false data injection (FDI) attacks. As a typical FDI attack, the zero trace stealthy (ZTS) attack has been recently disclosed for DCmGs, which can deteriorate the control objective while keeping stealthy to unknown input observer (UIO)-based detectors. In this work, we investigate the optimal deployment of ZTS attacks, where the adversary with limited resources aims to compromise a set of communication links such that the system state convergence error can be maximized. Specifically, we formulate the optimal ZTS attack deployment problem as a combinatorial optimization problem and unveil its NP-hard characteristic. Then, we discover the submodularity in the state convergence error function, enabling us to transform the original NP-hard problem into a tractable submodular maximization problem. Furthermore, based on the submodular optimization theory, we propose a novel distributed algorithm for the optimal ZTS attack deployment in DCmGs, which effectively balances the attack benefits and computation cost. Finally, comparisons between the centralized and distributed algorithms are illustrated through extensive simulations.