Industrial Intrusion Detection Technology Based on One-dimensional Multi-scale Residual Network

Abstract

In order to solve the problem that the traditional intrusion detection algorithm cannot learn more information based on the traffic data effectively and the detection accuracy is not ideal, an intrusion detection algorithm based on the one-dimensional multi-scale residual network for industrial control systems is proposed. Firstly, the nondimensionalization of input data is realized by defining the centrosymmetric logarithmic function. Then, a one-dimensional multi-scale residual neural network model is constructed to learn the characteristic information of industrial control data, and through cross-validation, parameter tuning is realized to obtain the best model. The experimental results show that the accuracy of this method is 98.99% and the AUC score is 0.9984, which can effectively realize the intrusion detection function under the industrial control system.