Title: DAIS: deep artificial immune system for intrusion detection in IoT ecosystems

Authors: Vineeta Soni; Devershi Pallavi Bhatt; Narendra Singh Yadav; Siddhant Saxena

Addresses: Department of Information Technology, Manipal University Jaipur, Jaipur, Rajasthan, India ' Department of Information Technology, Manipal University Jaipur, Jaipur, Rajasthan, India ' Department of Information Technology, Manipal University Jaipur, Jaipur, Rajasthan, India ' Department of Information Technology, Manipal University Jaipur, Jaipur, Rajasthan, India

Abstract: IoT has risen rapidly over the past decade. Massive data flow in a dynamic, decentralised environment threatens data security. This study addresses machine learning issues in IoT intrusion detection. DAIS is a bio-inspired artificial immune system architecture. The DAIS technique replicates the innate immunity and self-adaptive immune processes, which secures the dynamic IoT environment from existing and novel 'zero-day' assaults. The proposed DAIS architecture outperforms existing data-centric intrusion detection approaches and achieves benchmark accuracy of 99.87% on the MQTTset dataset and 87.64% on the imbalanced KDD-CUP-99 dataset. This means the proposed architecture is more robust to real-world attack scenarios and provides an end-to-end pipeline to secure the dynamic and evolving IoT network ecosystem.

Keywords: artificial immune systems; AIS; machine learning; intrusion detection; IoT networks; data security; statistics; neural networks.

DOI: 10.1504/IJBIC.2024.137904

International Journal of Bio-Inspired Computation, 2024 Vol.23 No.3, pp.148 - 156

Accepted: 10 Jul 2023
Published online: 08 Apr 2024 *

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