PHASE:用于下一代智能个性化智能医疗系统的安全分析仪

Nur Imtiazul Haque, M. Rahman
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引用次数: 0

摘要

随着互联医疗系统的出现,现代医疗系统正在经历快速转型,以应对不断增长的医疗需求。医疗物联网(IoMT)网络和植入式医疗设备(imd)正逐步被医疗机构采用,以提高效率和减少治疗延迟,从而产生智能医疗系统(SHS)。此外,通过SHS获得个性化医疗保健概念正在促进实时精准用药。然而,从身体传感器设备(bsd)收集的IoMT传感器测量数据的开放网络通信容易受到测量操作攻击,因为由于计算限制,它们主要使用轻量级加密算法进行加密或加密。因此,分析SHS的鲁棒性和实时传感器测量的脆弱性分析对于防止误用至关重要。提出了一种新的基于个性化规则的SHS实时安全分析框架PHASE。我们的框架可以为测量更改攻击合成最佳攻击向量,每个攻击向量代表最小的更改,以错误的患者健康状态误导SHS控制器。所识别的攻击向量可以在攻击者能力变化的情况下实时评估测量的脆弱性。我们使用皮马印第安人糖尿病、AIM-94和哈佛Dataverse数据集验证了所提出框架的有效性。
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PHASE: Security Analyzer for Next-Generation Smart Personalized Smart Healthcare System
With the advent of the connected healthcare systems, the contemporary healthcare system is going through a swift transformation to handle the ever-growing healthcare needs. The internet of medical things (IoMT) network and implantable medical devices (IMDs) are progressively being adopted in healthcare facilities for increasing efficiency and reducing treatment latency, thus giving rise to a smart healthcare system (SHS). Moreover, the acquisition of the personalized healthcare concept with SHS is boosting precise medication in real-time. However, the open network communication of IoMT sensor measurements collected from body sensor devices (BSDs) is vulnerable to measurement manipulation attacks since they are primarily encrypted or enciphered with lightweight cryptographic algorithms due to computational constraints. Hence, it is crucial to analyze the robustness of the SHS and real-time sensor measurements' vulnerability analysis to prevent mistreatment. This paper presents PHASE, a novel real-time security analysis framework for personalized rule-based SHS. Our framework can synthesize optimal attack vectors for measurement alteration attacks, each representing minimal required alterations to misinform the SHS controller with wrong patients' health status. The identified attack vectors can assess the vulnerability of the measurements in real-time with variable attacker's capability. We verify the effectiveness of the proposed framework using Pima Indians Diabetes, AIM-94, and Harvard Dataverse datasets.
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