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

TL;DR

This paper introduces a reinforcement learning-based method for real-time detection of cyber-attacks in smart grids, addressing limitations of existing sample-based and model-dependent approaches.

Contribution

It formulates the attack detection as a POMDP and proposes a universal, model-free RL algorithm for robust online detection in smart grids.

Findings

RL algorithm effectively detects cyber-attacks promptly.

The method outperforms traditional detection schemes.

Numerical results validate robustness and accuracy.

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.