Reinforcement Learning Approach to Generate Zero-Dynamics Attacks on Control Systems Without State Space Models

Stealthy attacks on control systems are bound to go unnoticed, which makes them a severe threat to critical infrastructure such as power systems, smart grids, and vehicular networks. This paper investigates a subset of stealthy attacks known as zero-dynamics-based stealthy attacks. While previous works on zero-dynamics attacks have highlighted the necessity of highly accurate knowledge of the system’s state space for generating attack signals, our study requires none. We propose a deep reinforcement learning based attacker to generate attack signals without prior knowledge of the system’s state space. We develop several attackers and detectors iteratively until the attacker and detectors no longer improve. In addition, we also show that the reinforcement learning based attacker successfully executes an attack in the same manner as the theoretical attacker described in previous literature.