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DOI: 10.14569/IJACSA.2022.0130338
PDF

Supervised Learning Techniques for Intrusion Detection System based on Multi-layer Classification Approach

Author 1: Mansoor Farooq

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 3, 2022.

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Abstract: The goal of this study is to discover a solution to two problems: first, the signature-based intrusion detection system SNORT can identify a new attack signature without human intervention; and second, signature-based IDS cannot detect multi-stage attacks. The interesting aspect of this study is the growing ways to address the aforementioned issues. We introduced a multi-layer classification strategy in this study, in which we employ two layers, the first of which is based on a decision tree, and the second of which includes machine learning technique fuzzy logic and neural networks. If the first layer fails to identify fresh attacks, the second layer takes over and detects new signature assaults, updating the SNORT signature automatically.

Keywords: IDS; SNORT; fuzzy logic; neural networks; decision tree; Naïve Bayes

Mansoor Farooq, “Supervised Learning Techniques for Intrusion Detection System based on Multi-layer Classification Approach” International Journal of Advanced Computer Science and Applications(IJACSA), 13(3), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130338

@article{Farooq2022,
title = {Supervised Learning Techniques for Intrusion Detection System based on Multi-layer Classification Approach},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130338},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130338},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {3},
author = {Mansoor Farooq}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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