An extended false data injection attack via deep reinforcement learning : attack model and countermeasures in cyber-physical power systems

Abstract

False data injection attacks are commonly used to evade the bad data detector in cyber-physical power systems. This paper proposes an extended attack strategy and a deep reinforcement learning-based detection method. Traditional false data injection attacks aim to remain stealthy and avoid detection by conventional detection mechanisms. An extended load attack is introduced to increase the potential for damage. Directly adding an extended component directly to the measurement makes it easily detectable by bad data detector. Accordingly, the extended attack integrates the added component into the state variables to improve stealth. An optimization model for the extended components of the proposed attack is developed, along with a homologous matrix. Additionally, an online attack detection scheme is formulated as a partially observable Markov decision process problem. A deep reinforcement learning-based detection framework is proposed, featuring a compound reward designed to minimize false alarms and time delays. The proposed online detector extracts state features under varying operating conditions and generates a policy to determine whether the power grid is under attack. An extended Euclidean distance indicator and an adaptive weight matrix are also proposed in the dynamic state estimation to improve estimation or detection accuracy. Numerical experiments validate the effectiveness and robustness of the proposed deep reinforcement learning-based detection scheme in power systems.