Enhancing Network Traffic Analysis with a Unique Imbalanced Data Algorithm in Deep Learning

Cyber security depends on network traffic analysis to identify anomalies and stop intrusions. Vast network data contains seldom occurring harmful activities which traditional models struggle to detect traffic patterns in. The research created an innovative deep learning system to solve class imbalance problems while producing superior network traffic classification results. The proposed framework combines adaptive data augmentation with deep learning approaches together with cost-sensitive learning and feature optimization methods to boost detection capabilities. Results show that our proposed model surpasses conventional models through more precise metrics and improved performance measures including F1-score and AUC-ROC metrics when tested against benchmark datasets. Testing outcomes demonstrate that our system effectively detects minority class occurrences while maintaining high classification achievements. The research presents a scalable monitoring solution for real traffic inspection which enhances network security analytics technologies.