  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}

Networking and Internet Architecture;Machine Learning;Performance