Volume 10 • Issue 1 • PP: 28–36 • 2025
Network Requests Classification Using Advanced Metaheuristic Optimization for Enhanced Network Security Systems
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© 2025 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
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The importance of network security has greatly increased in the continuously changing modern digital environment. Network security is a multi-layered defense mechanism that protects networks, data, and systems from unauthorized access and other malicious activities. As cyber threats become more advanced, traditional protective measures may no longer be adequate, and intrusion-detection systems must rapidly evolve. This research proposes BBERSC (Balance Between Al-Biruni Earth Radius Optimization and Sine Cosine Algorithm), which combines the local-search capability of Al-Biruni Earth Radius Optimization with the probabilistic improvement of the Sine Cosine Algorithm. The approach tunes Random Forest hyperparameters, including the number of trees, maximum depth, and minimum samples, to improve pattern recognition in network traffic. ANOVA and Wilcoxon signed-rank tests validate the effectiveness of the models and algorithms and assess differences between them. The Random Forest model within the BBERSC framework achieves an accuracy of 0.9901719, exceeding the other evaluated machine-learning algorithms.
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References
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