Host-based intrusion detection system using Support Vector Machine

Saifullah Memon; Asghar Ali; Waqas Ali; Muhammad Awais Rajput; Abbas Ali Ghoto; Muhammad Aamir Bhutto

doi:10.52584/QRJ.2001.07

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Host-based intrusion detection system using Support Vector Machine

Author(s)

Saifullah Memon State Key Laboratory of Networking and Switching Technology,BUPT, Beijing, China

Asghar Ali School of Engineering and Information Technology,UNSW, Australia

Waqas Ali School of Information Engineering, Yangzhou University,Jiangsu, China

Muhammad Awais Rajput Department of Artificial Intelligence, QUEST, Nawabshah, Pakistan

Abbas Ali Ghoto Department of Mathematics & Statistics, QUEST,Nawabshah, Pakistan

Muhammad Aamir Bhutto Department of Computer System Engineering,QUEST, Nawabshah, Pakistan

Abstract

In line with the communication industry's use of recent advancements in network technology to link remote areas of the world, attackers or intruders have stepped up their attacks on networking infrastructure. System administrators might deploy intrusion detection tools and systems to thwart such eforts. In recent years, the use of machine learning (ML) techniques in intrusion detection systems (IDSs) has increased. One of the most popular machine learning (ML) techniques for intrusion detection is the Support Vector Machine (SVM) due to its excellent generalization and capacity to escape the dimensionality curse. Recent studies have shown that the number of dimensions still impacts how well SVM-based intrusion detection systems work. The fact that SVM assesses all data characteristics equally has also caused some concerns. Actual intrusion detection datasets include a lot of redundant or superfluous characteristics. It would be ideal to consider feature weights while training an SVM. Knowledge Discovery in Databases (KDD) intrusion detection dataset ofers labeled data for the scientists and researchers; choosing the essential features or patterns from the input dataset makes the problem more straightforward and faster and acquires much more accuracy towards threat detection. Our work demonstrates the eficiency of recognizing the essential input patterns to design a more eficient Intrusion Detection System (IDS). Consequently, removing irrelevant or unimportant inputs makes the problem of detecting a threat simpler, faster, and more accurate. It has been an essential issue in intrusion detection that features selection and ranking must be made accordingly; it is the only way to detect intrusion accurately and eficiently. We implement the procedure to remove one feature at a time to run experiments on a Support Vector Machine (SVM) to grade the significance of the features for the KDD dataset. It has been observed that SVM-based IDSs utilizing fewer features could improve and eficiently perform.

Publication Details

Page(s) 44-54

DOI 10.52584/QRJ.2001.07

Published Journal: Quaid-e-Awam University Research Journal of Engineering, Science and Technology, Volume: 20, Issue: 1, Year: 2022

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Keywords

Support Vector Machine, Intrusion Detection System, Decision Trees, DoS, Hybrid intelligent system., Knowledge Discovery in Databases

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