An Anomaly Detection Method Based on Normalized Mutual Information Feature Selection and Quantum Wavelet Neural Network

This paper presents an anomaly detection model based on normalized mutual information feature selection (NMIFS) and quantum wavelet neural network (QWNN). The goal of the proposed model is to address the problem of determining the feature subset used to detect an anomaly in a machine learning task. In order to achieve an effective reduction for high-dimensional feature data, the NMIFS method is used to select the best feature combination from a given set of sample features. Then, the best combination of feature vectors are sent to the QWNN classifier for learning and training in the training phase, and the anomaly detection model is obtained. At the detection stage, the data is fed into the detection model and ultimately generates accurate detection results. The learning algorithm of structural risk minimization extreme learning machine is employed by the QWNN classifier to account for empirical and confidence risk. The experimental results on real abnormal data demonstrate that the NMIFS–QWNN method has higher detection accuracy and a lower false negative rate than the existing common anomaly detection methods. Furthermore, the complexity of the algorithm is low and the detection accuracy can reach up to 95.8%.