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

机译:在医学互联网上的安全和隐私问题的系统审查; 机器学习方法的作用

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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.
机译:背景的医疗物联网(IoMTs)的互联网正在逐步取代传统的医疗保健系统。然而,很少有人注意支付给他们的安全需求在IoMT设备和系统的发展。其中一个主要的原因可能是调整传统的安全解决方案,以IoMT系统的难度。机器学习(ML)已经在攻击检测和缓解的过程中成功应用。高级ML技术也可以解决现有和预期IoMT安全和隐私问题有前途的方法。然而,由于IoMT制度存在的挑战,当务之急是要懂得这些技术可以有效地利用,以满足安全和隐私需求,而不会影响IoMT系统的质量,服务和设备的使用寿命。方法论这篇文章是专门由ML技术IoMT及其解决方案的安全和隐私问题进行系统的文献回顾(SLR)。 2010年和2020年之间传播的最新研究论文从多个数据库中选择和标准化SLR方法进行。总共153份文件审查和对所选择的文件进行严格的分析。此外,本次审查研究试图突出的目前的方法和目标的限制,以找到对他们可能的解决方案。因此,详细的分析是通过着眼于它们的方法,优点,限制所使用的工具和数据进行所选择的文件。据观察结果ML技术已经显著部署的设备和网络层的安全性。目前大多数研究改进传统的衡量标准,而在他们的评价忽略复杂性能指标。他们的研究环境和使用的数据勉强表示IoMT系统。因此,如果不考虑指标,如资源的复杂性和功率使用传统的ML技术可能失败。

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