Enhanced Network Traffic Monitoring and Anomaly Detection System Utilizing Convolutional Neural Networks

With the rapid growth of network traffic and the continuous evolution of cyber attack vectors, traditional network traffic monitoring and anomaly detection methods are facing increasing challenges. In order to improve network security and traffic management efficiency, this paper proposes an enhanced network traffic monitoring and anomaly detection system based on convolutional neural network. Existing convolutional neural network model, the system enhances the ability to identify complex network traffic patterns by introducing a multi-level feature extraction module and an adaptive anomaly threshold adjustment mechanism. The model adopts the advantages of convolutional neural network in temporal feature extraction and spatial feature learning, captures the complex rules in the traffic data through the deep convolutional layer, and uses the attention mechanism to weight the important features, so as to further improve the accuracy and robustness of anomaly detection. In addition, the system introduces an end-to-end training method, which can adaptively optimize the detection effect in different network environments. Experimental results show that the proposed model has significant advantages in accuracy and real-time.