Research on Communication Network Attack Detection and Defense Mechanism Based on Deep Learning

In order to deal with the increasing complexity of network attacks, a CNN attack detection and defense mechanism is proposed. Firstly, by constructing an integrated network data set that includes normal traffic and several typical attacks (DDoS, MITM, malicious software propagation, etc.), the CNN is used for automatic analysis of network traffic. The CNN model can extract high dimensional features from the original data of network traffic and identify the attack type. To increase the precision of the model, the CNN structure is optimized and the hyperparameters are adjusted. Experiments show that CNN based network attack detection model achieves 98.1% accuracy and 97.3% of F1-SCORE, which is obviously superior to the conventional SVM, decision tree, etc. In terms of defense mechanism, combined with the attack detection results of the deep learning model, this paper designs a fast-response defense system. The system can identify and effectively intercept 92% of attack traffic in real time, and the false alarm rate is kept below 2.5%, ensuring the stable operation of the network. The response time of the model is 50 milliseconds, which fully meets the high real-time requirements of modern communication networks. In addition, this study also explores the defense strategy against attacks, which further improves the robustness of the system.