aspg-2-3586-1791419324 20261008002844 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Cybersecurity and Information Management JCIM 2769-7851 2690-6775 2025 16 1 Mohammed B. Al-Doori Department of Computer Networking Systems, College of Computer Sciences and Information Technology, University of Anbar, Al Anbar, Ramadi, Iraq Khattab M. Ali Alheeti Department of Computer Networking Systems, College of Computer Sciences and Information Technology, University of Anbar, Al Anbar, Ramadi, Iraq In this research, we investigate sophisticated methods for Intrusion Detection Systems (IDS) and Intrusion Prevention Systems (IPS), leveraging AI-based feature optimization and diverse machine learning strategies to bolster network intrusion detection and prevention. The study primarily utilizes the NSL-KDD dataset, an enhanced version of the KDD Cup 1999 dataset, chosen for its realistic portrayal of various attack types and for addressing the shortcomings of the original dataset. The methodology includes AI-based feature optimization using Particle Swarm Optimization and Genetic Algorithm, focusing on maximizing information gain and entropy. This is integrated with the use of Random Forest (RF) to reduce class overlapping, further enhanced by boosting techniques. Grey Wolves Optimization (GWO) alongside Random Forest. This innovative approach, inspired by grey wolf hunting strategies, is employed for classification tasks on the NSL-KDD dataset. The performance metrics for each intrusion class are meticulously evaluated, revealing that the GWO-RF combination achieves an accuracy of 0.94, precision of 0.95, recall of 0.93, and an F1 score of 0.94. 2025 01 14 3586 https://creativecommons.org/licenses/by/4.0/ 10.54216/JCIM.160101 https://www.americaspg.com/journal/2/article/3586 https://www.americaspg.com/storage/21739852601.pdf