Online Cyber-Attack Detection in Smart Grid: A Reinforcement Learning Approach
Machine Learning
2019-02-19 v1 Cryptography and Security
Machine Learning
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.
Cite
@article{arxiv.1809.05258,
title = {Online Cyber-Attack Detection in Smart Grid: A Reinforcement Learning Approach},
author = {Mehmet Necip Kurt and Oyetunji Ogundijo and Chong Li and Xiaodong Wang},
journal= {arXiv preprint arXiv:1809.05258},
year = {2019}
}