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

Abstract

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.

Citation

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. https://doi.org/10.1186/s40537-023-00805-5

• 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
• DOI: https://doi.org/10.1186/s40537-023-00805-5
• Keywords: Internet of Things; Artificial neural network; Network; Security requirements; Internet security
• Public URL: https://hull-repository.worktribe.com/output/4366887
• 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.