The study of network intrusion detection based on cnn-gru-twd

Network intrusion detection technology is one of the important technologies to ensure the security of industrial production, civil and other fields. In recent years, the amount of Internet data is growing rapidly, and the ways of network intrusion behavior are also evolving. An intrusion detection algorithm with strong detection ability, rapid response, good generalization and active defense function is designed and developed, which has broad application development space. Considering the characteristics of network traffic data in time and space, an intrusion detection model based on Convolutional Neural Networks (CNN) and Gated Recurrent Unit (GRU) is proposed. It can quickly detect the spatial and temporal characteristics of traffic data. And for the problem of low classification accuracy of network data, the three-way decision theory is introduced into the study of intrusion prevention system, and an intrusion detection algorithm based on CNN-GRU and three-way decision is designed. The models are verified by CSE-CIC-IDS 2018 data set. Experimental results show that the model based on CNN-GRU-TWD can achieve better results in the classification of sufficient training samples, and the accuracy, precision, recall and F1 value are higher than other comparison models. Compared with the model CNN, the model based on CNN-GRU-TWD has an accuracy of about 0.7 percentage points, and the precision, recall and F1 value are about 2 percentage points higher. It can be seen that the proposed CNN-GRU-TWD solves the problem of over-fitting in training and effectively detects network traffic data.