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  1. Free, publicly-accessible full text available August 1, 2024
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  4. null (Ed.)
    Abnormal event detection with the lowest latency is an indispensable function for safety-critical systems, such as cyber defense systems. However, as systems become increasingly complicated, conventional sequential event detection methods become less effective, especially when we need to define indicator metrics from complicated data manually. Although Deep Neural Networks (DNNs) have been used to handle heterogeneous data, the theoretic assurability and explainability are still insufficient. This paper provides a holistic framework for the quickest and sequential detection of abnormalities and time-dependent abnormal events. We explore the latent space characteristics of zero-bias neural networks considering the classification boundaries and abnormalities. We then provide a novel method to convert zero-bias DNN classifiers into performance-assured binary abnormality detectors. Finally, we provide a sequential Quickest Detection (QD) scheme that provides the theoretically assured lowest abnormal event detection delay under false alarm constraints using the converted abnormality detector. We verify the effectiveness of the framework using real massive signal records in aviation communication systems and simulation. Codes and data are available at. 
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  5. null (Ed.)
    The ubiquitous of 5G New Radio (5G NR) accelerates the massive implementations in many fields including swarm Unmanned Aircraft System (UAS) networking. The ultra capacities of 5G NR can provide more sufficient networking services for the swarm UAS networking which can enable swarm UAS to deploy in more complex and challenging scenarios to achieve missions. However, the conventional swarm UAS networking are mainly centralized or hierarchical which is vulnerable to the dynamics and the deployment of swarm UAS networking on a large scale. In this paper, we formulate a cell wall communications for the heterogeneous swarm UAS networking with the inspiration of biological cell wall communication. Fueled by reinforcement learning, we resolve the edge-coloring problem of cell wall communication scheduling to achieve the maximum throughput between the heterogeneous swarm UAS networking globally. The evaluation shows our proposed reinforcement learning enabled algorithm can surpass the conventional scheduling algorithms over 90% when the time piece is less than 0.01s and achieve the optimal throughput for the heterogeneous swarm UAS networking. 
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  6. null (Ed.)