Real-time detection method for mobile network traffic anomalies considering user behavior security monitoring

The traditional network traffic anomaly detection method is based on the principle of feature extraction and matching for a large amount of abnormal traffic data to achieve traffic anomaly detection. Due to the fast changing speed of mobile networks, it is difficult to ensure the real-time and accuracy of the detection method simply by extracting traffic features. To address the above problems, the study considers the real-time detection method of mobile network traffic anomaly for user behavior security monitoring. User behavior data is captured based on the network usage data of users provided by mobile network providers. Protocol parsing and application identify user data packets and extract user behavior features. A Bayesian classifier is constructed and a HAST-NAD model is used to achieve real-time detection of network traffic anomalies. Simulation experimental results show that the highest detection time of the detection method is only 104s, and the detection accuracy of the method is better than the traditional detection method, and the detection effect is better.