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Augmented Ebola Optimization Algorithm for Anomaly-Based Intrusion Detection System
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Author(s): Mohammad Alshinwan (Faculty of Information Technology, Applied Science Private University, Amman, Jordan), Abdallah Ehab Awad (Applied Science Private University, Jordan), Ali Ghassan Mohammad (MEU Research Unit, Middle East University, Amman, Jordan), Yousef Salah Alnajjar (MEU Research Unit, Middle East University, Amman, Jordan), Ahmed Ali Otoom (Applied Science Private University, Jordan), Radwan Batyha (Faculty of Information Technology, Applied Science Private University, Amman, Jordan), Mohammad Hijjawi (Faculty of Information Technology, Applied Science Private University, Amman, Jordan), Saad Said Alqahtany (Islamic University of Madinah, Madinah, Saudi Arabia), Omar husain Tarawneh (Faculty of Computer Sciences and Informatics, Amman Arab University, Amman, Jordan)and Laith Abualigah (Computer Science Department, Al Al-Bayt University, Mafraq, Jordan)
Copyright: 2025
Pages: 24
Source title:
Cryptography, Biometrics, and Anonymity in Cybersecurity Management
Source Author(s)/Editor(s): Mohammed Amin Almaiah (Department of Computer Science, The University of Jordan, Jordan)and Said Salloum (School of Science, Engineering, and Environment, University of Salford, UK)
DOI: 10.4018/979-8-3693-8014-7.ch015
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Abstract
The exponential growth of information technology results in a higher proliferation of internet-connected products. In addition, there was a rise in the Number of network attacks. An intrusion detection system (IDS) is considered one of the most efficient security solutions. This research presents a novel anomaly-based intrusion detection system (IDS) model that utilizes the Ebola Optimization Algorithm (EOA) technique for increased performance. The EOA algorithm is hybridized with the Prairie Dog Algorithm (PD) to enhance the performance of the EOA algorithm, called EOA-PD. The EOA-PD technique is used as a feature selection mechanism to determine the most pertinent elements from the dataset that lead to a high level of classification accuracy. In addition, a support vector machine is utilized to assess the effectiveness of the chosen features in correctly forecasting the attacks. Furthermore, The NSL-KDD dataset is used to showcase the efficacy of the suggested methodology across several attack scenarios.
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