Authors:
Hasan Abdulrazzaq Jawad,Shurooq M Abdulkhudhur,Rand A. Atta,Zahraa Ibrahim Abed,DOI NO:
https://doi.org/10.26782/jmcms.2026.04.00006Keywords:
Network Security Intrusion Detection System,Particle Swarm Optimization,Distributional Boosting Forest,Machine Learning,Cyberattack Detection,Real-Time Threat Monitoring,Abstract
Network protection relies on machine learning-based systems that detect intrusions. The detection systems lose their effectiveness because they use multiple duplicate features, and their performance depends on the specific network traffic patterns and system operational requirements, which prevent real-time functioning. The research presents a PSO-DBF intrusion detection framework, which begins with Distributional Boosting Forest (DBF) as its first step to create two groups (C1 and C2) that display similar probabilistic characteristics through network traffic clustering. The research team uses Particle Swarm Optimization (PSO) to process each cluster when they complete their clustering process because the method helps them find the most valuable network attributes, which decrease feature duplication while enhancing the ability to distinguish different features. K-Nearest Neighbors (KNN) provides the best performance when conventional machine learning classifiers use optimized feature subsets for intrusion detection. The proposed framework demonstrated its efficiency through experiments that utilized recognized IDS datasets. PSO removed almost 50% of the initial features while keeping 18 features from NSL-KDD and 21 features from UNSW-NB15, achieving reduction rates of approximately 56 percent and 57 percent. The proposed PSO–DBF with KNN framework achieved 99.36% accuracy on NSL-KDD and 99.89% accuracy on UNSW-NB15, exceeding the performance of Support Vector Machine (SVM), Naive Bayes (NB), Quadratic Discriminant Analysis (QDA), deep neural models, and recent hybrid metaheuristic-based IDS frameworks. The main improvement of the proposed method comes from its ability to reduce detection times, which drop from 0.44 milliseconds to 0.29 milliseconds. The DBF-PSO framework achieves its optimal performance for intrusion detection in enterprise cloud and edge-network security environments because of its detection accuracy and energy efficiency.Refference:
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