  The Internet of Things (IoT) is reshaping modern society by allowing a decent
number of RF devices to connect and share information through RF channels.
However, such an open nature also brings obstacles to surveillance. For
alleviation, a surveillance oracle, or a cognitive communication entity needs
to identify and confirm the appearance of known or unknown signal sources in
real-time. In this paper, we provide a deep learning framework for RF signal
surveillance. Specifically, we jointly integrate the Deep Neural Networks
(DNNs) and Quickest Detection (QD) to form a sequential signal surveillance
scheme. We first analyze the latent space characteristic of neural network
classification models, and then we leverage the response characteristics of DNN
classifiers and propose a novel method to transform existing DNN classifiers
into performance-assured binary abnormality detectors. In this way, we
seamlessly integrate the DNNs with the parametric quickest detection. Finally,
we propose an enhanced Elastic Weight Consolidation (EWC) algorithm with better
numerical stability for DNNs in signal surveillance systems to evolve
incrementally, we demonstrate that the zero-bias DNN is superior to regular DNN
models considering incremental learning and decision fairness. We evaluated the
proposed framework using real signal datasets and we believe this framework is
helpful in developing a trustworthy IoT ecosystem.
