Performance Evaluation of Classification Algorithms for Intrusion Detection on NSL-KDD Using Rapid Miner

Zoraez Ashfaq Malik, Marriam Siddiqui,Azhar Imran, Aman Ullah Yasin, Abdul Haleem Butt,Zahir JavedParacha ()
Additional contact information
Zoraez Ashfaq Malik, Marriam Siddiqui,Azhar Imran, Aman Ullah Yasin, Abdul Haleem Butt,Zahir JavedParacha: Department of Creative Technologies, Faculty of Computing & AI (Air University, Islamabad). Department of Electrical Engineering, Pakistan Institute of Electrical Engineering, Multan

International Journal of Innovations in Science & Technology, 2022, vol. 4, issue 1, 135-146

Abstract: The rapid advancement of the internet and its exponentially increasing usage has also exposed it to several vulnerabilities. Consequently, it has become an extremely important that can prevent network security issues. One of the most commonly implemented solutions is Intrusion Detection System (IDS) that can detect unusual attacks and unauthorized access to a secured network. In the past, several machine learning algorithms have been evaluated on the KDD intrusion dataset. However, this paper focuses on the implementation of the four machine learning algorithms: KNN, Random Forest, gradient boosted tree and decision tree. The models are also implemented through the Auto Model feature to determine its convenience. The results show that Gradient Boosted trees have achieved the highest accuracy (99.42%) in comparison to random forest algorithm that achieved the lowest accuracy (93.63%).

Keywords: Intrusion Detection; Machine Learning; Rapid Miner; NSL-KDD; Intrusion Detection System (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journal.50sea.com/index.php/IJIST/article/view/101/610 (application/pdf)
https://journal.50sea.com/index.php/IJIST/article/view/101 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:abq:ijist1:v:4:y:2022:i:1:p:135-146

Access Statistics for this article

International Journal of Innovations in Science & Technology is currently edited by Prof. Dr. Syed Amer Mahmood

More articles in International Journal of Innovations in Science & Technology from 50sea
Bibliographic data for series maintained by Iqra Nazeer ().