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

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.

References in corpus (2)

• Real-Time Detection of Hybrid and Stealthy Cyber-Attacks in Smart Grid
• State-of-the-Art in Sequential Change-Point Detection

Cited by in corpus (7)

• Reinforcement Learning for IoT Security: A Comprehensive Survey
• Machine Learning for Intrusion Detection in Industrial Control Systems: Applications, Challenges, and Recommendations
• Secure Distributed Dynamic State Estimation in Wide-Area Smart Grids
• Learning Near-Optimal Intrusion Responses Against Dynamic Attackers
• Multi-Sensor Sequential Change Detection with Unknown Change Propagation Pattern
• Intrusion Prevention through Optimal Stopping
• Learning Intrusion Prevention Policies through Optimal Stopping