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

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

14 Sep 2018arXiv:1809.05258archive 2025-07-28

Mehmet Necip Kurt, Oyetunji Ogundijo, Chong Li, Xiaodong Wang

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.

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Tasks

Anomaly Detection Cyber Attack Detection Outlier Detection Reinforcement Learning Reinforcement Learning (RL)reinforcement-learning

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ACGPN AWD-LSTM Activation Regularization DropConnect Dropout Embedding Dropout LSTM Sigmoid Activation Tanh Activation Temporal Activation Regularization Variational Dropout Weight Tying

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