Network Traffic Congestion Prediction done using Machine Learning

Too much traffic on networks has become a significant problem for communication systems, leading to slower network performance, worse QoS and uneasy user experiences in many types of infrastructure. As networks advance at lightning speed, from the first (1G) analog to the fifth (5G) generation, the rising complexity of network data calls for using advanced monitoring and predictive techniques. Even though legacy network traffic monitors detect issues in real-time and spot intrusions, they tend to lack the ability to predict congestion which is important for being proactive. This work introduces a new method for congestion prediction using machine learning which offers a solution to the problems that plague standard reactive ways of handling network traffic. The study combines information from earlier network monitoring techniques with the latest predictive models to make a solid approach for avoiding network congestion before it happens. We rely on excellent network software like Wireshark, TCPDump and Snort and also add machine learning methods to pick up on and forecast likely future network patterns. By merging common network monitoring and predictive network analytics, this research forms the base for networks that can maintain the best performance despite increases in complexity. This research should affect the operations and decisions of network, service and organization managers in all generations of communication networks.