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Malicious behavior monitoring of embedded medical devices

机译:嵌入式医疗设备的恶意行为监控

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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.
机译:本研究论文提出并分析了一种基于硬件的规范规则,用于检测嵌入在医疗器械中的传感器和执行器的恶意行为,其中患者的安全性至关重要,至关重要。该研究包括四种类型的医疗设备,即生命体征监测器(VSM),患者镇痛(PCA),心脏装置(CD)和连续葡萄糖监测器(CGM)器件。该研究基于一种方法,将设备的行为规则转换为状态机。我们设计了一个有限状态机(FSM)模型的转换行为规则,为每个设备构建行为规范规则监视(BSRM)工具。 Mentor Graphics Altera Modelsim和Quartus II软件包用于检查转换状态机的有效性。通过我们的模拟和综合,我们证明了BSRM工具可以有效地识别设备的预期正常行为,并检测与其正常行为的任何偏差。此外,该模型与较低功耗和更高带宽应用的要求一致。 BSRM的FPGA模块可以嵌入医疗设备中,以便检测到与行为规范的任何偏差。此外,FPGA芯片的可重新配置性质对所设计的模型增加了额外的优势,其中根据设备,患者,治疗算法和/或普遍的医疗保健应用程序的要求,可以轻松更新和量身定制行为规则。

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