ISSN :2582-9793
Mourad Benmalek and Kamel-Dine Haouam
Adv. Artif. Intell. Mach. Learn., 4 (3):2575-2592
Mourad Benmalek : Computer Engineering Department, College of Engineering and Architecture, Al Yamamah University
Kamel-Dine Haouam : Computer Engineering Department, College of Engineering and Architecture. Al Yamamah University
DOI: https://dx.doi.org/10.54364/AAIML.2024.43150
Article History: Received on: 11-Jul-24, Accepted on: 19-Sep-24, Published on: 26-Sep-24
Corresponding Author: Mourad Benmalek
Email: m_benmalek@yu.edu.sa
Citation: Mourad Benmalek, Kamel-Dine Haouam. (2024). Advancing Network Intrusion Detection Systems with Machine Learning Techniques. Adv. Artif. Intell. Mach. Learn., 4 (3 ):2575-2592.
This paper presents an approach to enhancing the
efficiency and effectiveness of Network Intrusion Detection Systems (NIDS) by
leveraging Machine Learning (ML) techniques, specifically Decision Trees (DT),
Naïve Bayes (NB), and Support Vector Machine (SVM). The proposed methodology
involves a comprehensive evaluation and comparison of these algorithms using
the NSL-KDD and UNSW-NB15 datasets, employing standard evaluation metrics such
as accuracy, precision, recall, and F1-score. The study identifies the most
effective algorithm for practical NIDS deployment. By providing actionable
insights and recommendations for implementing the most suitable ML algorithm,
this research contributes significantly to the ongoing efforts in strengthening
network security against evolving cyber threats.