A systematic review of security and privacy issues in the internet of medical things; the role of machine learning approaches

Shilan S Hameed; Wan Haslina Hassan; Liza Abdul Latiff; Fahad Ghabban

doi:10.7717/peerj-cs.414

A systematic review of security and privacy issues in the internet of medical things; the role of machine learning approaches

PeerJ Comput Sci. 2021 Mar 23:7:e414. doi: 10.7717/peerj-cs.414. eCollection 2021.

Authors

Shilan S Hameed^{1

2}, Wan Haslina Hassan¹, Liza Abdul Latiff³, Fahad Ghabban⁴

Affiliations

¹ Malaysia-Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia.
² Directorate of Information Technology, Koya University, Koya, Kurdistan Region, Iraq.
³ Razak Faculty of Technology and Informatics, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia.
⁴ Information Systems Department, College of Computer Sciences and Engineering, Taibah University, Medina, Saudi Arabia.

Abstract

Background: The Internet of Medical Things (IoMTs) is gradually replacing the traditional healthcare system. However, little attention has been paid to their security requirements in the development of the IoMT devices and systems. One of the main reasons can be the difficulty of tuning conventional security solutions to the IoMT system. Machine Learning (ML) has been successfully employed in the attack detection and mitigation process. Advanced ML technique can also be a promising approach to address the existing and anticipated IoMT security and privacy issues. However, because of the existing challenges of IoMT system, it is imperative to know how these techniques can be effectively utilized to meet the security and privacy requirements without affecting the IoMT systems quality, services, and device's lifespan.

Methodology: This article is devoted to perform a Systematic Literature Review (SLR) on the security and privacy issues of IoMT and their solutions by ML techniques. The recent research papers disseminated between 2010 and 2020 are selected from multiple databases and a standardized SLR method is conducted. A total of 153 papers were reviewed and a critical analysis was conducted on the selected papers. Furthermore, this review study attempts to highlight the limitation of the current methods and aims to find possible solutions to them. Thus, a detailed analysis was carried out on the selected papers through focusing on their methods, advantages, limitations, the utilized tools, and data.

Results: It was observed that ML techniques have been significantly deployed for device and network layer security. Most of the current studies improved traditional metrics while ignored performance complexity metrics in their evaluations. Their studies environments and utilized data barely represent IoMT system. Therefore, conventional ML techniques may fail if metrics such as resource complexity and power usage are not considered.

Keywords: A review; Artificial intelligence; IoMT; Machine learning; Security and privacy; Smart health; Systematic review; The internet of medical things.

Grants and funding

The authors received no funding for this work.