Power Systems Intrusion Detection Using Novel Wrapped Feature Selection Framework

Abstract

The power system measurement data has high-dimensional features and strong noise, which is difficult to be directly used for intrusion detection. Traditional feature extraction and selection methods take the feature processing as a preprocessing step and perform separately from the model training, which makes the features not well adapted to the model. Therefore, we propose a novel wrapped feature selection framework based on the Las Vegas algorithm, which can improve the detection accuracy by strengthening the coupling between the feature selection and the model training. The Las Vegas algorithm can evaluate a feature subset through a specified model. In this paper, a heuristic thinking is integrated into the Las Vegas algorithm, which greatly improves the search performance of the original method. Finally, the proposed framework is examined on the IEEE 14-bus, 39-bus and 57-bus test systems for two attacks and the experimental results proves the effectiveness and stability of the proposed framework.