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Engineering the advances of the artificial neural networks (ANNs) for the security requirements of Internet of Things: a systematic review

Ali, Yasir; Ullah Khan, Habib; Khalid, Muhammad


Yasir Ali

Habib Ullah Khan


Internet of Things (IoT) driven systems have been sharply growing in the recent times but this evolution is hampered by cybersecurity threats like spoofing, denial of service (DoS), distributed denial of service (DDoS) attacks, intrusions, malwares, authentication problems or other fatal attacks. The impacts of these security threats can be diminished by providing protection towards the different IoT security features. Different technological solutions have been presented to cope with the vulner-abilities and providing overall security towards IoT systems operating in numerous environments. In order to attain the full-pledged security of any IoT-driven system the significant contribution presented by artificial neural networks (ANNs) is worthy to be highlighted. Therefore, a systematic approach is presented to unfold the efforts and approaches of ANNs towards the security challenges of IoT. This systematic literature review (SLR) is composed of three (3) research questions (RQs) such that in RQ1, the major focus is to identify security requirements or criteria that defines a full-pledge IoT system. This question also focusses on pinpointing the different types of ANNs approaches that are contributing towards IoT security. In RQ2, we highlighted and discussed the contributions of ANNs approaches for individual security requirement/ feature in comprehensive and detailed fashion. In this question, we also determined the various models, frameworks, techniques and algorithms suggested by ANNs for the security advancements of IoT. In RQ3, different security mechanisms presented by ANNs especially towards intrusion detection system (IDS) in IoT along with their performances are comparatively discussed. In this research, 143 research papers have been used for analysis which are providing security solutions towards IoT security issues. A comprehensive and in-depth analysis of selected studies have been made to understand the current research gaps and future research works in this domain.


Ali, Y., Ullah Khan, H., & Khalid, M. (2023). Engineering the advances of the artificial neural networks (ANNs) for the security requirements of Internet of Things: a systematic review. Journal Of Big Data, 10(1), Article 128.

Journal Article Type Article
Acceptance Date Jul 18, 2023
Online Publication Date Aug 14, 2023
Publication Date 2023
Deposit Date Aug 24, 2023
Publicly Available Date Aug 24, 2023
Journal Journal of Big Data
Print ISSN 2196-1115
Publisher SpringerOpen
Peer Reviewed Peer Reviewed
Volume 10
Issue 1
Article Number 128
Keywords Internet of Things; Artificial neural network; Network; Security requirements; Internet security
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Additional Information Received: 4 February 2023; Accepted: 18 July 2023; First Online: 14 August 2023; : ; : Ethical approval obtained from Department of Accounting and Information Systems, College of Business and Economics, Qatar University, Doha Qatar. All the authors are providing consent for participating.; : All authors are providing consent for publishing.; : The authors declare no competing interests.


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© The Author(s) 2023.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit

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