Campus Network Intrusion Detection Based on Gated Recurrent Neural Network and Domain Generation Algorithm

Abstract

Network attacks are diversified, rare and Universal generalization. This has made the exploration and construction of network information flow packet threat detection systems, which becomes a hot research topic in preventing network attacks. So this study establishes a network data threat detection model based on traditional network threat detection systems and deep learning neural networks. And convolutional neural network and data enhancement technology are used to optimize the model and improve rare data recognizing accuracy. The experiment confirms that this detection model has a recognition probability of approximately 11% and 42% for two rare attacks when N=1, respectively. When N=2, their probabilities are 52% and 78%, respectively. When N=3, their recognition probabilities are approximately 85% and 92%, respectively. When N=4, their recognition probabilities are about 58% and 68%, respectively, with N=3 having the best recognition effect. In addition, the recognition efficiency of this model for malicious domain name attacks and normal data remains around 90%, which has significant advantages compared to traditional detection systems. The proposed network data flow threat detection model that integrates Gated Recurrent Neural Network and Domain Generation Algorithm has certain practicality and feasibility.