Deep Learning-Enhanced Intrusion and Malware Detection: A Network Security Defense Framework

With the rapid development of information technology, network security has gradually become one of the important challenges facing the world. Traditional network security defense methods have been unable to cope with the increasingly complex and diversified network attacks. Therefore, the network security defense strategy based on deep learning has gradually attracted the attention of researchers. This paper proposes a network security defense framework based on deep learning, which uses deep learning models such as convolutional neural network (CNN), short term memory network (LSTM) and generative adversarial network (GAN) to improve the capability of network intrusion detection, malicious behavior identification and abnormal traffic monitoring. The experimental results show that the proposed deep learning framework achieves an accuracy of 90.4% in the malware classification task, which is significantly improved compared to the 78.5% accuracy of traditional machine learning methods such as SVM and random forest. In DDoS attack detection, the accuracy of the CNN-based model reached 92.7%, far higher than the traditional method of 84.3%. In the abnormal traffic detection based on LSTM, F1 scores 0.91, which proves the advantages of the model in time series data processing. In addition, the robustness and defense capability of the model can be effectively improved by generating false attack samples generated by the adversarial network. The research in this paper not only provides a new idea and framework for network security defense based on deep learning, but also provides guidance for future research to improve the real-time and precision of the model.