Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-04-24 Cooperative journals: 《计算机应用研究》
Abstract: Aiming at typical attack types of industrial control networks, this paper proposed a method of predicting communication anomalies in industrial networks using deep learning. First, the principal component analysis of the raw data reduction and eliminated the correlation between the original data set. Secondly, build artificial neural networks and to optimize the input weights and threshold limits the use of machine learning. The fish swarm algorithm was improved by the idea of particle inertia mass calculation in the gravitational search algorithm. The test experiment results show that the accuracy of anomaly detection is improved, and the detection times are effectively shortened. And realizes the purpose of making use of the depth learning to predict the abnormal behavior of communication in industrial networks.