保护医疗保健物联网中的设备

Áine MacDermott, P. Kendrick, I. Idowu, Mal Ashall, Q. Shi
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引用次数: 16

摘要

物联网(IoT)对电子医疗、辅助生活、以人为中心的传感和健康产生了积极影响。最近,这种互连被称为医疗保健物联网(H-IoT)。基于从连接的“事物”收集的信息的实时监控提供了大规模的连接,并更深入地了解患者护理、个人习惯和常规。虽然将这种模式引入医疗保健的好处是显而易见的,但基础设施和设备的潜在安全漏洞和威胁也不容忽视。H-IoT将对社会产生重大影响,随着攻击者已经以无数种方式利用物联网,物联网将不可避免地成为网络安全最脆弱的领域。在H-IoT中保护这些“东西”需要多方面的方法。通过使用机器学习进行预测分析,传达了一种用于高级持续威胁检测的多代理方法:识别安全漏洞,识别模式以进行预测和识别异常值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Securing Things in the Healthcare Internet of Things
The Internet of Things (IoT) has had a positive impact on e-health, assisted living, human-centric sensing and wellness. Recently this interconnection has been referred to as Healthcare IoT (H-IoT). Real-time monitoring based on the information gathered from the connected ‘things’ provides large scale connectivity and a greater insight into patient care, individual habits and routines. While the benefits of introducing this paradigm into healthcare are conspicuous, the underlying security vulnerabilities and threats of the infrastructure and devices cannot go unaddressed. H-IoT is set to impact society significantly, and with attackers already exploiting the IoT in a myriad of ways, it is inevitable that the IoT will become the most vulnerable area of cyber security. Securing these ‘things’ in H-IoT requires a multi-faceted approach. A multi-agent approach to advanced persistent threat detection is conveyed with the use of machine learning for predictive analytics: identifying security vulnerabilities, identifying patterns in order to make predictions and identify outliers.
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