嵌入式医疗设备的恶意行为监控

Razan Abdulhammed, M. Faezipour, K. Elleithy
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引用次数: 2

摘要

本文提出并分析了一种基于硬件规范规则的方法,用于检测嵌入在医疗设备中的传感器和执行器的恶意行为,其中患者的安全至关重要。本研究包括四种医疗器械,分别是生命体征监护仪(VSM)、患者镇痛控制仪(PCA)、心脏监护仪(CD)和连续血糖监护仪(CGM)。这项研究基于一种将设备的行为规则转换为状态机的方法。将转换后的行为规则设计为有限状态机模型,为每个设备构建行为规范规则监控(BSRM)工具。使用Mentor Graphics Altera ModelSim和Quartus II软件包检查转换后的状态机的有效性。通过我们的仿真和综合,我们证明了BSRM工具可以有效地识别器件的预期正常行为并检测其正常行为的任何偏差。此外,该模型符合低功耗和高带宽应用的要求。BSRM的FPGA模块可以嵌入到医疗设备中,从而可以检测到任何与行为规范的偏差。此外,FPGA芯片的可重构特性为设计的模型增加了额外的优势,其中行为规则可以根据设备、患者、治疗算法和/或普遍的医疗保健应用程序的要求轻松更新和定制。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Malicious behavior monitoring of embedded medical devices
This research paper proposes and analyzes a hardware based specification rules approach for detecting malicious behaviors of sensors and actuators embedded in medical devices in which the safety of the patient is critical and of utmost importance. The study includes four types of medical devices, namely the Vital Sign Monitor (VSM), Patient Analgesic Control (PCA), Cardiac Device (CD), and Continuous Glucose Monitor (CGM) devices. The research is based on a methodology that transforms a device's behavior rules into a state machine. We design a Finite State Machine (FSM) model out of transformed behavior rules to build a Behavior Specification Rules Monitoring (BSRM) tool for each device. Mentor Graphics Altera ModelSim and Quartus II software packages are used to check the validity of the transformed states machines. Through our simulation and synthesis, we demonstrate that the BSRM tool can effectively identify the expected normal behavior of the device and detect any deviation from its normal behavior. Furthermore, the model is consistent with the requirements for lower power consumption and higher bandwidth applications. The FPGA module of the BSRM can be embedded in the medical devices so that any deviation from the behavior specification can be detected. Moreover, the reconfigurable nature of the FPGA chip adds an extra advantage to the designed model in which the behavior rule can be easily updated and tailored according to the requirements of the device, patient, treatment algorithm, and/or pervasive healthcare applications.
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