A Novel Wrapped Feature Selection Framework for Developing Power System Intrusion Detection Based on Machine Learning Methods

The power system measurement data has high-dimensional features and strong noise, which is difficult to be directly used for intrusion detection. Traditional machine learning methods used in the power system intrusion detection take the feature processing as a preprocessing step and perform separately from the training, which makes the features not well adapted to the training. Therefore, this article proposes a novel binary particle swarm-wrapped feature selection optimization framework (BPSWO), which can improve the intrusion detection accuracy of machine learning methods by strengthening the coupling between the feature selection and the training. First, the improved transfer function is used to make the method converge to the global optimal particle. Second, the chaotic transformation and the Hamming distance are used to solve the premature problem of the traditional particle swarm optimization. Then, the different classifiers can be embedded in the particle swarm for training. The BPSWO trains the classifier while selecting features and the final training classifier is used for intrusion detection. The proposed method is examined on the public power system from Oak Ridge National Laboratory, USA and the IEEE 57-bus system. Compared with the existing power system intrusion detection methods based on machine learning, the experimental results show that the BPSWO can achieve the state-of-the-art in the detection accuracy, which proves the effectiveness and stability of the proposed method.