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

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

Received 18 July 2021; Accepted 26 August 2021; Issue published 17 November 2021

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.

Keywords

Kitsune; machine learning; active wiretap; ARP MitM; fuzzing; OS scan; SSDP flood; SYNDoS; SSL renegotiation; and video injection

Cite This Article

A. Alabdulatif and S. Sajjad Hussain Rizvi, "Machine learning approach for improvement in kitsune nid," Intelligent Automation & Soft Computing, vol. 32, no.2, pp. 827–840, 2022.



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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