University network intrusion detection method based on deep learning

With the increasing number of network attacks, the network security of universities has been threatened unprecedentedly. This study explores a deep learning-based method for network intrusion detection in colleges and universities. Firstly, the development of network intrusion detection technology is reviewed, and the characteristics of university network environment are analyzed emphatically. Furthermore, the quality and representativeness of the data are ensured through sophisticated data preprocessing strategies. On this basis, an appropriate deep learning model is selected, and the configuration is optimized for the university network environment. After the system was deployed in real time, it was verified on multiple data sets, and the results showed that the model had high accuracy, precision and recall, showing strong generalization ability. The conclusion part confirms the effectiveness of deep learning technology in network intrusion detection in colleges and universities, and provides a new research direction and practical reference for the field of network security.