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

Author(s)
Zoraez Ashfaq Malik Department of Creative Technologies, Faculty of Computing & AI (Air University, Islamabad, Pakistan
Marriam Siddiqui Department of Creative Technologies, Faculty of Computing & AI (Air University, Islamabad, Pakistan
Azhar Imran Department of Creative Technologies, Faculty of Computing & AI (Air University, Islamabad, Pakistan
Aman Ullah Yasin Department of Creative Technologies, Faculty of Computing & AI (Air University, Islamabad, Pakistan
Abdul Haleem Butt Department of Creative Technologies, Faculty of Computing & AI (Air University, Islamabad, Pakistan
Zahir Javed Paracha Department of Electrical Engineering, Pakistan Institute of Electrical Engineering, Multan, Pakistan
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%).
Publication Details
Page(s) 135-146
DOI 10.33411/IJIST/2022040110
Published Journal: International Journal of Innovations in Science & Technology, Volume: 4, Issue: 1, Year: 2022

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Keywords
machine learning, RapidMiner, NULLIntrusion detection system, NSL-KDD and Gradient boosted tree.
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