Incremental Anomaly Detection with Guarantee in the Internet of Medical Things
Incremental Anomaly Detection with Guarantee in the Internet of Medical Things
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DOI：
10.1145/3576842.3582374
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发表时间：
2023-05
期刊：
影响因子：
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通讯作者：
Xiayan Ji;Hyonyoung Choi;O. Sokolsky;Insup Lee
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作者：
Xiayan Ji;Hyonyoung Choi;O. Sokolsky;Insup Lee
医疗物联网（IoMT）在支持学习的组件的帮助下，在健康监测中变得越来越重要。然而，基于iom的系统必须高度可靠，因为它直接与患者互动。促进可靠IoMT的一个关键功能是异常检测，这涉及到在医疗设备的使用模式偏离正常行为时发送警报。由于IoMT的安全关键性质，异常检测器期望始终具有高准确性和低错误，理想情况下是有保证的。此外，由于基于iom的系统是非平稳的，异常检测器和性能保证必须适应不断变化的数据分布。为了应对这些挑战，我们提出了一个基于可能近似正确（PAC）的双边保证的IoMT增量异常检测框架，并以人在环设计为指导，以适应异常分布的变化。因此，我们的框架可以提高检测性能，并为虚警率（FAR）和漏警率（MAR）提供严格的保证。我们使用合成数据和现实世界的IoMT监测平台VitalCore来证明我们设计的有效性。
The Internet of Medical Things (IoMT), aided by learning-enabled components, is becoming increasingly important in health monitoring. However, the IoMT-based system must be highly reliable since it directly interacts with the patients. One critical function for facilitating reliable IoMT is anomaly detection, which involves sending alerts when a medical device’s usage pattern deviates from normal behavior. Due to the safety-critical nature of IoMT, the anomaly detectors are expected to have consistently high accuracy and low error, ideally being bounded with a guarantee. Besides, since the IoMT-based system is non-stationary, the anomaly detector and the performance guarantee should adapt to the evolving data distributions. To tackle these challenges, we propose a framework for incremental anomaly detection in IoMT with a Probably Approximately Correct (PAC)-based two-sided guarantee, guided by a human-in-the-loop design to accommodate shifts in anomaly distributions. As a result, our framework can improve detection performance and provide a tight guarantee on False Alarm Rate (FAR) and Miss Alarm Rate (MAR). We demonstrate the effectiveness of our design using synthetic data and the real-world IoMT monitoring platform VitalCore.