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Deep learning-based security behaviour analysis in IoT environments: A survey

Yue, Yawei; Li, Shancang; Legg, Phil; Li, Fuzhong

Deep learning-based security behaviour analysis in IoT environments: A survey Thumbnail


Yawei Yue

Shancang Li
Senior Lecturer in Computer Forensics and Security

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Dr Phil Legg
Associate Professor in Cyber Security

Fuzhong Li


Internet of Things (IoT) applications have been used in a wide variety of domains ranging from smart home, healthcare, smart energy, and Industrial 4.0. While IoT brings a number of benefits including convenience and efficiency, it also introduces a number of emerging threats. The number of IoT devices that may be connected, along with the ad hoc nature of such systems, often exacerbates the situation. Security and privacy have emerged as significant challenges for managing IoT. Recent work has demonstrated that deep learning algorithms are very efficient for conducting security analysis of IoT systems and have many advantages compared with the other methods. This paper aims to provide a thorough survey related to deep learning applications in IoT for security and privacy concerns. Our primary focus is on deep learning enhanced IoT security. First, from the view of system architecture and the methodologies used, we investigate applications of deep learning in IoT security. Second, from the security perspective of IoT systems, we analyse the suitability of deep learning to improve security. Finally, we evaluate the performance of deep learning in IoT system security.


Yue, Y., Li, S., Legg, P., & Li, F. (2021). Deep learning-based security behaviour analysis in IoT environments: A survey. Security and Communication Networks, 2021, 1-13.

Journal Article Type Article
Acceptance Date Dec 15, 2020
Online Publication Date Jan 8, 2021
Publication Date Jan 8, 2021
Deposit Date Jan 11, 2021
Publicly Available Date Jan 14, 2021
Journal Security and Communication Networks
Print ISSN 1939-0114
Electronic ISSN 1939-0122
Publisher Hindawi
Peer Reviewed Peer Reviewed
Volume 2021
Article Number 8873195
Pages 1-13
Keywords Computer Networks and Communications; Information Systems
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