Machine Learning Methods for Attack Detection in Smart Grid

Abstract

Abstract: In the realm of smart grids attack detection, statistical learning poses challenges across various attack scenarios, whether measurements are obtained either online or in batch mode. This approach categorizes measurements into two groups: secure and attacked, leveraging machine learning algorithms. The suggested method offers a framework for detecting attacks, aiming to address limitations arising from the sparse nature of the problem and leveraging any available past system knowledge. Through decision- and feature-level fusion, established batch and online learning methods are employed to tackle the attack detection challenge. To uncover unobservable attacks using statistical learning techniques, the relationships between the geometric and statistical characteristics of the attack vectors within the attack scenarios and the learning algorithms are scrutinized