Artificial Intelligence for Real-Time Network Monitoring: A Comprehensive Review

The rise of modern networks like software-defined networking (SDN), the Internet of Things (IoT), and fifth-generation (5G) communications has created significant difficulties in real-time monitoring and management. Traditional methods such as SNMP and NetFlow have become inadequate because they struggle with scalability, lack accuracy in detecting unknown threats, and cause processing delays. Recently, artificial intelligence (AI) has emerged as a promising approach to enhance network monitoring, thanks to machine learning and deep learning. Research indicates that models such as decision trees, random forests, and XGBoost achieve better accuracy in intrusion detection for SDN. Meanwhile, methods based on CNNs, LSTMs, and GANs can effectively identify complex traffic patterns and greatly decrease false alarms. Furthermore, AI plays a crucial role in cybersecurity, acting as the first line of defense against new threats. Studies also show that in the 5G context, using time-series analysis and automated algorithms for network management can lower latency, improve quality of service (QoS), and boost scalability. In IoT and edge computing settings, lightweight and adaptive protocols are recommended to extend network coverage and save energy. Overall, the literature suggests that merging AI with real-time network monitoring improves both security and efficiency. It also paves the way for automation and predictive analysis, aiding the evolution of future networks. Nonetheless, challenges such as the need for extensive training datasets, high computational demands, and privacy concerns continue to hinder widespread adoption of this technology.