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Intrusion Detection Framework for the Internet of Things Using a Dense Random Neural Network

Latif, Shahid; Huma, Zil e; Jamal, Sajjad Shaukat; Ahmed, Fawad; Ahmad, Jawad; Zahid, Adnan; Dashtipour, Kia; Aftab, Muhammad Umar; Ahmad, Muhammad; Abbasi, Qammer Hussain


Shahid Latif

Zil e Huma

Sajjad Shaukat Jamal

Fawad Ahmed

Adnan Zahid

Muhammad Umar Aftab

Muhammad Ahmad

Qammer Hussain Abbasi


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-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.

Journal Article Type Article
Online Publication Date Nov 24, 2021
Publication Date 2022-09
Deposit Date Jun 20, 2022
Publicly Available Date Jun 20, 2022
Journal IEEE Transactions on Industrial Informatics
Print ISSN 1551-3203
Electronic ISSN 1941-0050
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 18
Issue 9
Pages 6435-6444
Keywords Cybersecurity, deep learning, dense random neural network (DnRaNN), Internet of Things (IoT), intrusion detection
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