Application of Machine Learning in Computer Networks: Techniques, Datasets, and Applications for Performance and Security Optimization

This study designs and tests a network security system based on a combined Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) framework. In this study, distributed processing and reinforcement learning methods in combination with differential privacy are introduced into the proposed system to enhance attack detection and network management. The evaluation results show significant improvements; 97.3% detection accuracy, 34% more efficient bandwidth utilization and 45% less latency than the previous system. The 16-node linear scalability of the distributed architecture has a throughput of 1.2 million packets per second. It is defended against adversarial attacks by maintaining accuracy above 92% and provides a total energy saving of 38% using dynamic batch processing. Three months of testing in an operational environment detected 99.2% of 1,247 threats, including 23 new attack types, with an average detection time of 1.8 seconds. Sensitivity analysis was performed to preserve the privacy of sensitive data while maintaining network performance. The results show that the hybrid solution is reliable, scalable and secure for today's network management.