Journal of Artificial Intelligence and Metaheuristics
Journal DOI
2833-5597ISSN (Online)
Volume 10 , Issue 1 , PP: 72-87, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Marwa M. Eid 1 *
Doi: https://doi.org/10.54216/JAIM.100104
The importance of network security has greatly been enhanced in the modern digital environment that continuously changes. Network security, on the other hand, is a multi-layered defense mechanism that seeks to protect networks, data, and systems from malpractices such as unauthorized access breaches or activities. Cyber threats become ever more advanced, and traditional protective measures can no longer prove to be adequate. Given the necessity of such a threat to adapt and be intelligent, an active intrusion detection system must necessarily rapidly evolve its methods in response. The central element contained in this research is the proposal of a novel algorithm, BBERSC (Balance Between Al Biruni Earth Radius Optimization and Sine Cosine Algorithm). This algorithm is carefully crafted to achieve a compromise between the means for local search provided by Al-Biruni Earth Radius Optimization and probabilistic improvement, which are characteristic of the Swine Cosine Algorithm. BBERSC brings forward the cause of harmonizing these two optimization methods to revolutionize model accuracy and credibility, which may be achieved for network security’s distinctiveness. One of the crucial elements of this study lies in the fact that hyperparameter tuning is quite a detailed process, especially for Random Forest. Parameters, including the number of trees, maximum depth, and minimum samples, are systematically employed to vary to augment pattern recognition capability by employing model processing network traffic. To ensure the validation of the effectiveness of the proposed models and algorithms, statistical analysis is carried out through ANOVA test & Wilcoxon Signed Rank Test. These tests show the models’ results through rigorous assessments and emphasize differences between them. As the conclusion of this study, It is displayed that the Random Forest model utilized inside BBERSC algorithmic framework facilitates operational accuracy level 0.9901719, which is incomparable among all other machine learning algorithms.
Network Requests , Al-Biruni Earth Radius Optimization , Sine Cosine Algorithm , Network Security , Intrusion Detection
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