Cyber-physical microgrids are vulnerable to rootkit attacks that manipulate system dynamics to create instabilities in the network. Rootkits tend to hide their access level within microgrid system components to launch sudden attacks that prey on the slow response time of defenders to manipulate system trajectory. This problem can be formulated as a multi-stage, non-cooperative, zero-sum game with the attacker and the defender modeled as opposing players. To solve the game, this paper proposes a deep reinforcement learning-based strategy that dynamically identifies rootkit access levels and isolates incoming manipulations by incorporating changes in the defense plan. A major advantage of the proposed strategy is its ability to establish resiliency without altering the physical transmission/distribution network topology, thereby diminishing potential instability issues. The paper also presents several simulation results and case studies to demonstrate the operating mechanism and robustness of the proposed strategy.

翻译：网络物理微电网易受根工具包攻击，此类攻击通过操控系统动态在网络中引发不稳定。根工具包倾向于隐藏其在微电网系统组件中的访问级别，以发起突然攻击，利用防御者响应缓慢的时间差来操纵系统轨迹。该问题可建模为一个多阶段、非合作、零和博弈，其中攻击者和防御者被设定为对弈双方。为求解该博弈，本文提出一种基于深度强化学习的策略，该策略通过动态识别根工具包访问级别并将防御计划的变更纳入考量，从而隔离传入的恶意操控行为。该策略的一大优势在于无需改变物理输电/配电网络拓扑即可建立韧性，进而减少潜在的不稳定问题。本文还通过多项仿真结果与案例研究，展示了所提策略的运行机制与鲁棒性。