NEURAL NETWORKS FOR NETWORKS: AI-POWERED TRAFFIC ANALYSIS AND DYNAMIC OPTIMIZATION

Abstract

With the exponential growth of digital communication, traditional network traffic management methods are struggling to keep pace with dynamic and unpredictable data flows. Artificial Intelligence (AI), particularly deep learning, offers innovative solutions for real-time traffic analysis and autonomous optimization of network resources. This research investigates the use of AI-based models, primarily neural networks, to classify, predict, and optimize network traffic flows automatically. A hybrid architecture integrating Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) is proposed for traffic feature extraction and temporal analysis. Experimental results demonstrate that AI-driven approaches can outperform conventional methods in anomaly detection, congestion avoidance, and Quality of Service (QoS) optimization. By combining traffic classification accuracy of 96.2% with adaptive optimization mechanisms, the proposed system reduces packet loss by 14% and improves throughput by 11%. These findings indicate that AIpowered network management systems have the potential to revolutionize next-generation networking by enabling real-time, self-optimizing infrastructures