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Intrusion Detection System: A Comparative Study of Machine Learning-Based IDS
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Author(s): Amit Singh (Government of India, India), Jay Prakash (Vijay Singh Pathik Government (PG) College, India), Gaurav Kumar (Bennett University, India), Praphula Kumar Jain (GLA University, India)and Loknath Sai Ambati (Oklahoma City University, USA)
Copyright: 2024
Volume: 35
Issue: 1
Pages: 25
Source title:
Journal of Database Management (JDM)
Editor(s)-in-Chief: Keng Siau (City University of Hong Kong, Hong Kong SAR)
DOI: 10.4018/JDM.338276
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Abstract
The use of encrypted data, the diversity of new protocols, and the surge in the number of malicious activities worldwide have posed new challenges for intrusion detection systems (IDS). In this scenario, existing signature-based IDS are not performing well. Various researchers have proposed machine learning-based IDS to detect unknown malicious activities based on behaviour patterns. Results have shown that machine learning-based IDS perform better than signature-based IDS (SIDS) in identifying new malicious activities in the communication network. In this paper, the authors have analyzed the IDS dataset that contains the most current common attacks and evaluated the performance of network intrusion detection systems by adopting two data resampling techniques and 10 machine learning classifiers. It has been observed that the top three IDS models—KNeighbors, XGBoost, and AdaBoost—outperform binary-class classification with 99.49%, 99.14%, and 98.75% accuracy, and XGBoost, KNneighbors, and GaussianNB outperform in multi-class classification with 99.30%, 98.88%, and 96.66% accuracy.
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