Dynamic Detection Model of Abnormal Traffic in University Network Based on Improved Deep Reinforcement Learning

Zhong Huang

With the rapid expansion of university network scale and the diversified development of user services, abnormal traffic detection has become a core issue to ensure the security of university information systems. Existing abnormal detection methods not only exhibit poor adaptability to dynamic traffic changes in campus networks but also have a high false positive rate when dealing with low-frequency abnormal traffic. To address the above problems, this study proposes a dynamic detection model based on improved Deep Reinforcement Learning (DRL). First, a traffic feature attention mechanism is introduced to enhance the ability to extract key abnormal features; second, the reward function of the DRL agent is optimized to achieve a balance between detection accuracy and real-time performance. Experimental results based on public datasets and actual campus network traffic data of universities show that the proposed model achieves an average detection accuracy of 98.7%, and the false positive rate is reduced by 12.3% compared with the standard DRL model, which verifies its effectiveness in abnormal traffic detection of campus networks.