Host-based intrusion detection system using Support Vector Machine

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 efforts. 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 offers 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 efficiency of recognizing the essential input patterns to design a more efficient 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 efficiently. 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 efficiently perform.