Intelligent Optimization of OSPF Path Selection Using Machine Learning Models for Adaptive Network Routing

At the core of enterprise networks lies routing protocols that make forwarding decisions based on a set of rules and metrics. One of the most popular and widely used routing protocols is the Open Shortest Path First (OSPF). Traditional OSPF calculates the cost of the route primarily based on interface bandwidth, without considering real-time factors such as latency, congestion, or link stability. These calculations are static and can lead to deficiencies in adapting to unstable network conditions. This study proposes the integration of multiple machine learning (ML) models and techniques to enhance OSPF routing decisions. Four important ML functions namely traffic forecast, anomaly detection, failure prediction, and dynamic cost optimization—have been used to improve OSPF performance. ML methods such as Random Forest and XGBoost are used to predict and assign costs in traffic utilization and real-time performance assessments. AutoRegressive Integrated Moving Average models and Long Short-Term Memory are applied to enable traffic predictions and route adjustments before potential congestions. Furthermore, link and node failure are common in network routing. Random Forest and logistic regression models are employed to predict these. The simulation took place in Graphical Network Simulator-3 using Cisco routers and Linux servers to allow thorough testing before and after applying the ML models. The results and findings have shown that the integration of ML models reroutes the traffic to enhance latency and throughput by approximately 30%. The findings demonstrate the upside of ML-enhanced OSPF routing as a versatile and scalable solution for high-demand networks.