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CFS-MHA: A Two-Stage Network Intrusion Detection Framework

CFS-MHA: A Two-Stage Network Intrusion Detection Framework

Ritinder Kaur, Neha Gupta
Copyright: © 2022 |Volume: 16 |Issue: 1 |Pages: 27
ISSN: 1930-1650|EISSN: 1930-1669|EISBN13: 9781683180203|DOI: 10.4018/IJISP.313663
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MLA

Kaur, Ritinder, and Neha Gupta. "CFS-MHA: A Two-Stage Network Intrusion Detection Framework." IJISP vol.16, no.1 2022: pp.1-27. http://doi.org/10.4018/IJISP.313663

APA

Kaur, R. & Gupta, N. (2022). CFS-MHA: A Two-Stage Network Intrusion Detection Framework. International Journal of Information Security and Privacy (IJISP), 16(1), 1-27. http://doi.org/10.4018/IJISP.313663

Chicago

Kaur, Ritinder, and Neha Gupta. "CFS-MHA: A Two-Stage Network Intrusion Detection Framework," International Journal of Information Security and Privacy (IJISP) 16, no.1: 1-27. http://doi.org/10.4018/IJISP.313663

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Abstract

With the increasing modernism in our society, networked computers are playing a pivotal role in dispersion of knowledge, and the protection of critical data in information systems has become a challenge for the research and industrial community. The intrusion detection systems undermine huge amounts of attack data to extrapolate patterns using machine learning techniques. In this paper, a two-stage intrusion detection model has been proposed to employ a blend of diverse attribute selection techniques and machine learning algorithms to provide high performance intrusion detection. The first stage extracts the relevant attributes by applying a hybrid meta-heuristic feature selection algorithm, and in the second stage, supervised machine learning algorithms have been implemented to improve the detection accuracy, execution time, and error rate. NSL-KDD dataset has been used, and the performance of CFS-MHA has been evaluated using different classification strategies. By using 10 attributes and random tree ensemble techniques, CFS-MHA has achieved an accuracy of 81.2% in detection of attacks.

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