Title: Experimentative analysis of artificial immune system algorithms for intrusion detection in IoT networks

Authors: Syed Ali Mehdi; Syed Zeeshan Hussain

Addresses: Department of Computer Science, Jamia Millia Islamia, New Delhi, India ' Department of Computer Science, Jamia Millia Islamia, New Delhi, India

Abstract: Intrusion detection systems (IDS) are the basic security line for any network. Internet of things (IoT) networks have been in trend and usage. It thus raises security challenges in IoT networks, and thus there is a requirement for IDS for IoT. There has been promising research on artificial immune system (AIS) algorithms for intrusion detection. In this paper, AIS algorithms, namely negative selection algorithm (NSA) and clonal selection algorithm (CSA), for intrusion detection in IoT are compared. These algorithms are evaluated using Python on a popular public BoT-IoT-L01 dataset. Various performance metrics like detection rate, classification accuracy, false-positive rate, and false-negative rate are used for comparison. The results of the research show that the CSA is better than the NSA-based intrusion detection for IoT in terms of accuracy. CSA achieved an overall detection rate of 91% in comparison to NSA's, with 79%. It was found that NSA was more efficient than CSA in detecting rare kinds of attacks. The research findings indicate that AIS can be a powerful tool for IoT-based IDS. The selection of an appropriate AIS algorithm for IDS in IoT depends on the specific requirements and characteristics of the IoT network.

Keywords: internet of things; IoT; artificial immune systems; AIS; intrusion detection system; IDS.

DOI: 10.1504/IJCVR.2025.148218

International Journal of Computational Vision and Robotics, 2025 Vol.15 No.5, pp.577 - 587

Received: 02 Oct 2023
Accepted: 25 Nov 2023
Published online: 01 Sep 2025 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article