Research on intelligent information security protection and threat detection model based on deep learning

With the rapid development of information technology and the increasing complexity of the network environment, the threats to information security are constantly upgrading, and the traditional means of protection and detection have been difficult to cope with diversified and intelligent attacks. In recent years, deep learning technology has shown broad application prospects in the field of information security due to its powerful feature extraction and pattern recognition capabilities. In this paper, we focus on the application of deep learning in intelligent information security protection and threat detection, firstly, we systematically sort out the basic theories and technical systems of deep learning in information security, secondly, we construct a threat detection model that integrates multiple deep neural network structures, and we introduce strategies such as data enhancement and migration learning to improve the detection accuracy and generalization ability. Through empirical analysis on multiple public datasets, the superior performance of the proposed model in intrusion detection, malicious behavior identification and other tasks is verified. The results show that deep learning technology can effectively improve the active protection capability and threat perception level of information systems, and provide a strong support for building an intelligent information security system.