Whale Optimization-Enhanced Support Vector Machine for Cyber Threat Detection and Classification

This paper presents a new hybrid machine learning-based framework for robust anomaly detection, specifically for critical network environments. This method is based on the implementation of an SVM classifier and Whale Optimization Algorithm (WOA), a bio-inspired metaheuristic technique, aimed at resolving the crucial bottleneck of classifier hyperparameter optimization. The main objective is to achieve a remarkable increase in classification accuracy and decrease the false positive rate (FPR), which are significant shortcomings in current security systems. WOA is used to optimize the critical parameters of SVM efficiently so as to optimize its abilities for discriminating between normal and malicious network activities. The SWaT (Secure Water Treatment) dataset, a high-fidelity benchmark for industrial control systems, was selected for the experimental evaluation. As a consequence of the results, the accuracy of the optimized WOA-SVM model reaches 95.3% and the false positive rate reduces to 3.6%. Our results validate how the optimization-enhanced implementation provides a more robust and accurate security mechanism for contemporary network infrastructures