Intelligent Routing Optimization for Enhanced Network Traffic Control
DOI:
https://doi.org/10.64882/ijrt.v13.i3.485Keywords:
Optimized Routing, Traffic Management, Network Performance, Route Selection Algorithm, Load Balancing, Communication EfficiencyAbstract
The primary objective of this research is to design a secure and intelligent routing framework that effectively detects and mitigates wormhole attacks while improving overall routing performance in Mobile Ad Hoc Networks (MANETs). Traditional routing protocols are highly vulnerable to wormhole intrusions, resulting in severe packet loss, malicious data manipulation and degraded communication reliability. To overcome these limitations, the study adopts a machine learning–based approach using four supervised classifiers—Decision Tree, Logistic Regression, Support Vector Machine and Random Forest—to identify abnormal routing behaviors. A simulated MANET testbed was created to generate both legitimate and wormhole attack traffic for training and evaluation. The framework is further enhanced with three optimization techniques—Modified Genetic Algorithm (MGA), Grey Wolf Optimizer (GWO) and Ant Colony Optimization (ACO)—to enable adaptive and efficient route selection under dynamic mobility. Experimental results show that the Random Forest model delivers the best performance, achieving 98.64% detection accuracy, 72.40% packet delivery rate and reducing stolen packets to 1%. Among hybrid models, RF + MGA provides the most balanced security and routing performance, RF + GWO achieves superior energy efficiency and RF + ACO ensures faster path convergence suitable for high-mobility scenarios. Overall, the proposed system significantly enhances network security, stability and sustainability, making it ideal for mission-critical MANET applications such as military operations, emergency communication and large-scale IoT deployments
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