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Annals of Data Science

23-05-2024

A Survey of Artificial Intelligence for Industrial Detection

Authors: Jun Li, YiFei Hai, SongJia Yin

Published in: Annals of Data Science

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Abstract

In the past decade, deep learning has greatly increased the complexity of industrial production intelligence by virtue of its powerful learning capability. At the same time, it has also brought security challenges to the field of industrial production information networks, mainly in two aspects: production safety and network information security. The former is mainly focused on ensuring the safety of personnel behavior in the production environment, including two different categories: detection of dangerous targets and identification of dangerous behaviors. The latter focuses on the safety of industrial information systems, especially networks. In recent years, deep learning-based detection techniques have made great strides in addressing these dual problems. Therefore, this paper presents an exhaustive study on the development of deep learning-based detection methods for industrial production safety analysis and information network security problem detection. The paper presents a comprehensive taxonomy for classifying production environments and production network information, classifying and clustering prevalent industrial security challenges, with a special emphasis on the role of deep learning in insecure behavior identification and information security risk detection.We provides an in-depth analysis of the advantages, limitations, and suitable application scenarios of these two approaches. In addition, the paper provides insights into contemporary challenges and future trends in this field and concludes with a discussion of prospects for future research.

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Title: A Survey of Artificial Intelligence for Industrial Detection
Authors: Jun Li
YiFei Hai
SongJia Yin
Publication date: 23-05-2024
Publisher: Springer Berlin Heidelberg
Published in: Annals of Data Science
Print ISSN: 2198-5804
Electronic ISSN: 2198-5812
DOI: https://doi.org/10.1007/s40745-024-00545-0

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