We propose a lightweight specification-based misbehavior detection management technique to efficiently and effectively detect misbehavior of an IoT device embedded in a medical cyber physical system through automatic model checking and formal verification. We verify our specification-based misbehavior detection technique with a patient-controlled analgesia (PCA) device embedded in a medical health monitoring system. Through extensive ns3 simulation, we verify its superior performance over popular machine learning anomaly detection methods based on support vector machine (SVM) and k-nearest neighbors (KNN) techniques in both effectiveness and efficiency performance metrics.

中文翻译：

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医疗网络物理系统中嵌入式物联网设备的轻量级不当行为检测管理

我们提出了一种基于轻量级规范的不当行为检测管理技术，通过自动模型检查和形式验证来高效有效地检测嵌入医疗信息物理系统的物联网设备的不当行为。我们使用嵌入在医疗健康监测系统中的患者自控镇痛 (PCA) 设备验证我们基于规范的不当行为检测技术。通过广泛的 ns3 模拟，我们在有效性和效率性能指标上验证了其优于基于支持向量机 (SVM) 和 k 最近邻 (KNN) 技术的流行机器学习异常检测方法的性能。