Attack Detection in IoT Network Using Support Vector Machine and Improved Feature Selection Technique

Abstract

As a result of the rapid advancement of technology, the Internet of Things (IoT) has emerged as an essential research question, capable of collecting and sending data through a network between linked items without the need for human interaction. However, these interconnected devices often encounter challenges related to data security, encompassing aspects of confidentiality, integrity, availability, authentication, and privacy, particularly when facing potential intruders. Addressing this concern, our study propose a novel host-based intrusion detection system grounded in machine learning. Our approach incorporates a feature selection (FS) technique based on the correlation between features and a ranking function utilizing Support Vector Machine (SVM). The experimentation, conducted on the NSL-KDD dataset, demonstrates the efficacy of our methodology. The results showcase superiority over comparable approaches in both binary and multi-class classification scenarios, achieving remarkable accuracy rates of 99.094% and 99.11%, respectively. This underscores the potential of our proposed system in enhancing security measures for IoT devices.