ENHANCED GREY WOLF OPTIMIZER WITH DYNAMIC ENCIRCLING AND FITNESS-BASED BLENDING FOR CYBERATTACK DETECTION USING SELF-ORGANIZING FUZZY NEURAL NETWORK
DOI:
https://doi.org/10.18623/rvd.v23.5674Keywords:
Cybersecurity, Wolf Optimization Algorithm, Stacked Fuzzy Neural Network, Precision, RecallAbstract
Intrusion Detection Systems (IDS) has an intrinsic role in protecting contemporary networks against developing cyberattacks. Still, current IDSs suffer from high false positives, low resistance to novel attacks, and inferior feature selection in heavy traffic. This paper introduces a new and improved Grey Wolf Optimizer (GWO) with adaptive mechanisms that enhance exploration and exploitation capabilities in complex search spaces. A sigmoid adaptive decay function and leader-spiral motion boost search efficiency and rate of convergence. A dynamic encircling mechanism weighted by fitness adjusts the search based on wolf fitness, increasing diversity and accuracy in the search. A Fitness Difference-Based Blending Mechanism (FDBM) enhances position updates by utilizing elite wolves' positional differences, maximizing flexibility and accuracy. The chosen features are treated with a Self-Organizing Stacked Fuzzy Neural Network (SOSFNN) to cope better with the dynamics and for better accuracy of detection in dynamic environments. The model provides 98.22% accuracy, 94.92% precision, 94.48% recall, and 94.67% F1-score, with a minimal false positive (0.0007 for UNSW-NB15 and 1.14% for CICIDS-2018). Our proposed improvements illustrate better detection efficiency and lower temporal complexity (27.10s for CICIDS-2018 and 15.10s for UNSW-NB15) compared to state-of-the-art studies, thus being highly accurate, adaptable, and effective for real-time IDS applications.
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