A high precision intrusion detection system for network security communication based on multi-scale convolutional neural network

The openness of network data makes it vulnerable to hackers, viruses and other attacks, which seriously threatens the privacy and property security of users. In order to improve the accuracy of the intrusion detection for network security communication, based on the traditional intrusion detection system, combining with the deep learning theory and shortcomings, this paper proposed an intrusion detection system for network security communication based on multi-scale convolutional neural network, and conducted the corresponding experiments on public data sets. The experimental results perform that compared to the intrusion detection system based on Adaboost model and Recurrent Neural Network model, the convergence speed of multi-scale convolutional neural network system is faster, the average error detection rate is reduced by 4.02%, and the average accuracy is improved by 4.37%. The results prove that the intrusion detection system based on multi-scale convolution neural network has a high detection accuracy. (c) 2021 Elsevier B.V. All rights reserved.