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

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

Abstract

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.

• 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
• DOI: https://doi.org/10.1155/2021/8873195
• Keywords: Computer Networks and Communications; Information Systems
• Public URL: https://uwe-repository.worktribe.com/output/6980752