Smart grid

Electrical engineering

Markov model

Sample (material)

Online learning

Mathematics

Outlier

Reinforcement learning

Markov chain

Data mining

Statistics

Markov decision process

World Wide Web

Machine learning

Engineering

Artificial intelligence

Markov process

Cyber-attack

Partially observable Markov decision process

Chemistry

Grid

Geometry

Computer science

Computer security

Chromatography

Anomaly detection

IEEE Transactions on Smart Grid

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.

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