Intrusion Detection Model for University Networks Based on BP Neural Network

In the context of Education Informatization 2.0, university networks, as a major carrier of informatization, play an increasingly important role and face greater security risks. Against this background, intrusion detection technology, as one of the important means of network security, has attracted more and more attention. Targeting various intrusion risks faced by university networks, and based on research on intrusion detection technology and BP neural networks, this paper combines BP neural networks with intrusion detection technology to propose an intrusion detection model for university networks based on BP neural networks. This model utilizes the BP neural network to identify network intrusion behaviors. On one hand, under-sampling combined with over-sampling methods are used for data balancing of network intrusion data; on the other hand, an adaptive learning rate is used to dynamically adjust the learning rate. These improvements enhance the model's accuracy and speed up model construction, ensuring better intrusion detection performance. Tested using data from the KDDCUP99 dataset, the results show that compared to the standard BP neural network model, the optimized BP neural network model has improved accuracy by 0.6% and reduced modeling time by 3.07 times, and it also performs better in terms of recall and precision rates for various types of data. This demonstrates that the model can speed up the modeling process and improve the accuracy of intrusion detection, making it practically valuable.