Building A Computer Network Traffic Warning Model Based on Multi-Strategy Sparrow Search Algorithm

The security of computer networks under the development of big data is facing serious challenges, and the real-time and effective protection requires the detection of network data to be effective. Therefore, the research starts from network anomaly detection, introduces Error Back Propagation (BP) and Tuna Swarm algorithm for performance improvement and dimensionality reduction of feature data from the target of anomaly detection accuracy and network traffic data processing, and optimizes BP network parameters with improved sparrow search algorithm to propose a detection and early warning system. The results show that the model has high convergence speed and the average recall and F-value in anomalous data detection reaches 29.568% and 61.742%, which is significantly better than other comparative algorithms, and the recognition accuracy of anomalous data types is much higher than 90%. The method can effectively provide reference value for network security protection management and information monitoring.