Deep reinforcement learning control approach to mitigating actuator attacks

This paper investigates the deep reinforcement learning based secure control problem for cyber–physical systems (CPS) under false data injection attacks. We describe the CPS under attacks as a Markov decision process (MDP), based on which the secure controller design for CPS under attacks is formulated as an action policy learning using data....

Training Strategies for Autoencoder-based Detection of False Data Injection Attacks

The security of energy supply in a power grid critically depends on the ability to accurately estimate the state of the system. However, manipulated power flow measurements can potentially hide overloads and bypass the bad data detection scheme to interfere the validity of estimated states. In this paper, we use an autoencoder neural network to...

False Data Injection Attacks on Hybrid AC/HVDC Interconnected Systems with Virtual Inertia-Vulnerability, Impact and Detection

Power systems are moving towards hybrid AC/DC grids with the integration of HVDC links, renewable resources and energy storage modules. The load frequency control (LFC) of tomorrow has to consider the complex interactions between these components. Meanwhile, more attention should be paid to cyber security concerns as the LFC loop highly...