A New Hybrid Approach for Improving Intrusion Detection System Based on Scatter Search Algorithm and Support Vector Machine

In recent years, the extensive growth of the Internet, coupled with the integration of sensors and wireless sensor networks into critical areas like healthcare and military defense, has led to a significant expansion in the use of artificial intelligence and the Internet of Things applications. Due to the importance and sensitivity of the data in these fields, it is crucial to use an intrusion detection system (IDS), which applies specific algorithms to analyze and process the data from the network in order to find any suspicious activities or behaviors on the system and improve system security. A network frequently carries enormous amounts of data, particularly in military applications where data must flow continuously. In this research, an enhanced IDS using the scatter search algorithm is proposed. A population of randomly generated initial solutions is used to produce a diverse group of selected and high-performing solutions, which serve as a reference set to steer the search process. This reference set is then adopted as a feature selection method based on a support vector machine. The efficiency of the suggested method was examined utilizing the NSL-KDD dataset, and the results were compared with the Gazelle Optimization Algorithm, Algorithmic Optimization Algorithm, Gray Wolf Optimizer, Adjusted Gray Wolf Optimizer, and Particle Swarm Optimization. The main performance metrics utilized to test the efficiency of the suggested method include accuracy, detection efficiency, false-positive rate, and feature count. The results illustrated that the suggested method has obtained a high intrusion detection accuracy in the IDS system of 99% and decreased false alarm rates (0.02) and selected only 17 features from the initial dataset, which contained 41 features, demonstrating the effectiveness of the suggested method. Received: 6 October 2024 | Revised: 14 January 2025 | Accepted: 13 February 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available from the corresponding author upon reasonable request. Author Contribution Statement Feras E. AbuAladas: Conceptualization, Software, Validation, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization, Project administration. Mohammad Shehab: Conceptualization, Validation, Formal analysis, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Rasha Israwah: Methodology, Validation, Investigation, Data curation, Writing – original draft, Writing – review & editing. Ghaith Jaradat: Methodology, Formal analysis, Writing – original draft, Writing – review & editing, Visualization. Yousef Qawqzeh: Software, Investigation, Writing – original draft, Writing – review & editing.