Abnormality detection is essential to the performance of safety-critical and latency-constrained systems. However, as systems are becoming increasingly complicated with a large quantity of heterogeneous data, conventional statistical change point detection methods are becoming less effective and efficient. Although Deep Learning (DL) and Deep Neural Networks (DNNs) are increasingly employed to handle heterogeneous data, they still lack theoretic assurable performance and explainability. This paper integrates zero-bias DNN and Quickest Event Detection algorithms to provide a holistic framework for quick and reliable detection of both abnormalities and time-dependent abnormal events in the Internet of Things (IoT). We first use the zero-bias dense layer to increase the explainability of DNN. We provide a solution to convert zero-bias DNN classifiers into performance assured binary abnormality detectors. Using the converted abnormality detector, we then present a sequential quickest detection scheme that provides the theoretically assured lowest abnormal event detection delay under false alarm constraints. Finally, we demonstrate the effectiveness of the framework using both massive signal records from real-world aviation communication systems and simulated data. Code and data of our work is available at \url{https://github.com/pcwhy/AbnormalityDetectionInZbDNN}

中文翻译：

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零偏差深度学习在物联网中实现快速可靠的异常检测

异常检测对于安全关键和延迟受限系统的性能至关重要。然而，随着大量异构数据的系统变得越来越复杂，传统的统计变化点检测方法变得越来越不有效和高效。尽管深度学习 (DL) 和深度神经网络 (DNN) 越来越多地用于处理异构数据，但它们仍然缺乏理论上可保证的性能和可解释性。本文集成了零偏差 DNN 和 Quickest 事件检测算法，为快速可靠地检测物联网 (IoT) 中的异常和时间相关异常事件提供了一个整体框架。我们首先使用零偏置密集层来增加 DNN 的可解释性。我们提供了一种将零偏差 DNN 分类器转换为性能有保证的二进制异常检测器的解决方案。使用转换后的异常检测器，我们然后提出了一种顺序最快的检测方案，该方案在误报约束下提供理论上保证的最低异常事件检测延迟。最后，我们使用来自真实世界航空通信系统的大量信号记录和模拟数据证明了该框架的有效性。我们工作的代码和数据可在 \url{https://github.com/pcwhy/AbnormalityDetectionInZbDNN} 获得 我们使用来自真实世界航空通信系统的大量信号记录和模拟数据证明了该框架的有效性。我们工作的代码和数据可在 \url{https://github.com/pcwhy/AbnormalityDetectionInZbDNN} 获得 我们使用来自真实世界航空通信系统的大量信号记录和模拟数据证明了该框架的有效性。我们工作的代码和数据可在 \url{https://github.com/pcwhy/AbnormalityDetectionInZbDNN} 获得