Intrusion detection framework for the Internet of Things using a dense random neural network

Latif, S., Huma, Z. e., Jamal, S. S., Ahmed, F., Ahmad, J., Zahid, A., Dashtipour, K., Aftab, M. U., Ahmad, M. and Abbasi, Q. H. (2022) Intrusion detection framework for the Internet of Things using a dense random neural network. IEEE Transactions on Industrial Informatics, 18(9), pp. 6435-6444. (doi: 10.1109/TII.2021.3130248)

Abstract

The Internet of Things (IoT) devices, networks, and applications have become an integral part of modern societies. Despite their social, economic, and industrial benefits, these devices and networks are frequently targeted by cybercriminals. Hence, IoT applications and networks demand lightweight, fast and flexible security solutions to overcome these challenges. In this regard, Artificial Intelligence (AI)-based solutions with big data analytics can produce promising results in the field of cybersecurity. This article proposes a lightweight Dense Random Neural Network (DnRaNN) for intrusion detection in the IoT. The proposed scheme is well suited for implementation in resource-constrained IoT networks due to its inherent improved generalization capabilities and distributed nature. The suggested model was evaluated by conducting extensive experiments on a new generation IoT security dataset ToN_IoT. All the experiments were conducted under different hyperparameters and the efficiency of the proposed DnRaNN was evaluated through multiple performance metrics. The findings of the proposed study provide recommendations and insights in binary class and multiclass scenarios. The proposed DnRaNN model attained attack detection accuracy of 99.14% and 99.05% for binary class and multiclass classifications, respectively.

• Item Type: Articles
• Additional Information: This work was supported in part by EPSRC IAA under Award EP/R511705/1. The work of Sajjad Shaukat Jamal was supported by the Deanship of Scientific Research at King Khalid University through research groups program under Grant R.G.P. 2/48/42.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Abbasi, Professor Qammer and Dashtipour, Dr Kia
• Authors: Latif, S., Huma, Z. e., Jamal, S. S., Ahmed, F., Ahmad, J., Zahid, A., Dashtipour, K., Aftab, M. U., Ahmad, M., and Abbasi, Q. H.
• College/School: College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
• Journal Name: IEEE Transactions on Industrial Informatics
• Publisher: IEEE
• ISSN: 1551-3203
• ISSN (Online): 1941-0050
• Published Online: 24 November 2021
• Copyright Holders: Copyright © 2021 IEEE
• First Published: First published in IEEE Transactions on Industrial Informatics 2022 18(9):6435-6444
• Publisher Policy: Reproduced under a Creative Commons licence

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
304896		EPSRC-IAA: Early Stage Commercialisation of a PET Imaging Agent for the Detection of Cardiovascular Disease and Cancer	Andrew Sutherland	Engineering and Physical Sciences Research Council (EPSRC)	EP/R511705/1	Chemistry