Online Cyber-Attack Detection in Smart Grid: A Reinforcement Learning Approach

Authors: Mehmet Necip Kurt, Oyetunji Ogundijo, Chong Li, Xiaodong Wang
Published: 2018-09-14

Source: https://arxiv.org/abs/1809.05258

PDF: https://arxiv.org/pdf/1809.05258

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オンライン学習 POMDP問題 状態遷移モデル

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Abstract

Early detection of cyber-attacks is crucial for a safe and reliable operation of the smart grid. In the literature, outlier detection schemes making sample-by-sample decisions and online detection schemes requiring perfect attack models have been proposed. In this paper, we formulate the online attack/anomaly detection problem as a partially observable Markov decision process (POMDP) problem and propose a universal robust online detection algorithm using the framework of model-free reinforcement learning (RL) for POMDPs. Numerical studies illustrate the effectiveness of the proposed RL-based algorithm in timely and accurate detection of cyber-attacks targeting the smart grid.