Network Intrusion Detection Model Based on Improved Convolutional Neural Network

Abstract

Intrusion detection technology is the key technology in network security. With the diversification of means of network attacks, the traditional intrusion detection technology has gradually revealed some problems, such as poor detection performance and low adaptability. In view of the existing problems, this paper constructs an intrusion detection model based on improved convolution neural network. The convolution neural network is further studied and improved. This paper optimizes the initial weights of convolutional neural network by genetic algorithm on the problems of slow training speed and difficult convergence in the training of convolutional neural network. The experimental results show that the convergence speed of the convolution neural network optimized by genetic algorithm is faster and the feature extraction ability is strengthened. Convolution neural network based on genetic algorithm can detect various kinds of abnormal data and attack types effectively, and also has the ability to detect new attack data.