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Machine Learning Approach for Improvement in Kitsune NID

Abdullah Alabdulatif1, Syed Sajjad Hussain Rizvi2,*

1Department of Computer, College of Sciences and Arts in Al-Rass, Qassim University, Al-Rass, Saudi Arabia
2 Shaheed Zulfikar Ali Bhutto Institute of Science and Technology, Karachi, Pakistan

* Corresponding Author: Syed Sajjad Hussain Rizvi. Email: email

(This article belongs to the Special Issue: Humans and Cyber Security Behaviour)

Intelligent Automation & Soft Computing 2022, 32(2), 827-840. https://doi.org/10.32604/iasc.2022.021879

Abstract

Network intrusion detection is the pressing need of every communication network. Many network intrusion detection systems (NIDS) have been proposed in the literature to cater to this need. In recent literature, plug-and-play NIDS, Kitsune, was proposed in 2018 and greatly appreciated in the literature. The Kitsune datasets were divided into 70% training set and 30% testing set for machine learning algorithms. Our previous study referred that the variants of the Tree algorithms such as Simple Tree, Medium Tree, Coarse Tree, RUS Boosted, and Bagged Tree have reported similar effectiveness but with slight variation inefficiency. To further extend this investigation, we have explored the performance of variants of above said Tree algorithms on other datasets provided by Kitsune, such as Active Wiretap, ARP MitM, Fuzzing, OS Scan, SSDP Flood, SYN DoS, SSL renegotiation, Mirai, and Video Injection. This investigation ascertains the likely performance of above said tree algorithm variants. After a deep and rigorous analysis, the Fine Tree is highly recommended for the improved version of the Kitsune Tool.

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APA Style
Alabdulatif, A., Rizvi, S.S.H. (2022). Machine learning approach for improvement in kitsune NID. Intelligent Automation & Soft Computing, 32(2), 827-840. https://doi.org/10.32604/iasc.2022.021879
Vancouver Style
Alabdulatif A, Rizvi SSH. Machine learning approach for improvement in kitsune NID. Intell Automat Soft Comput . 2022;32(2):827-840 https://doi.org/10.32604/iasc.2022.021879
IEEE Style
A. Alabdulatif and S.S.H. Rizvi, “Machine Learning Approach for Improvement in Kitsune NID,” Intell. Automat. Soft Comput. , vol. 32, no. 2, pp. 827-840, 2022. https://doi.org/10.32604/iasc.2022.021879



cc Copyright © 2022 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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