Deep Learning and Industrial Internet Security: Application and Challenges

Industrial Internet security is crucial for strengthening the manufacturing and network sectors of China. Deep learning, owing to its strong expression ability, good adaptability, and high portability, can support the establishment of an industrial Internet security system and method that is intelli...

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Autor principal:	Yang Chen, Ma Ruicheng, Wang Yushi, Zhai Yanlong, Zhu Liehuang
Formato:	article
Lenguaje:	ZH
Publicado:	《中国工程科学》杂志社 2021
Materias:	industrial Internet security, Internet of Things security, deep learning, data security Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/3f57a6428ccc4fcb9ba923cc734ff4b8
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spelling	oai:doaj.org-article:3f57a6428ccc4fcb9ba923cc734ff4b82021-11-29T08:00:34ZDeep Learning and Industrial Internet Security: Application and Challenges2096-003410.15302/J-SSCAE-2021.02.013https://doaj.org/article/3f57a6428ccc4fcb9ba923cc734ff4b82021-02-01T00:00:00Zhttp://www.engineering.org.cn/en/10.15302/J-SSCAE-2021.02.013https://doaj.org/toc/2096-0034Industrial Internet security is crucial for strengthening the manufacturing and network sectors of China. Deep learning, owing to its strong expression ability, good adaptability, and high portability, can support the establishment of an industrial Internet security system and method that is intelligent and autonomous. Therefore, it is of great value to promote the integrated innovation of deep learning and industrial Internet security. In this study, we analyze the development demand for industrial Internet security from the perspective of macro industrial environment, security technology, and deep learning system, and summarize the application status of deep learning to industrial Internet security in terms of device, control, network, application, and data layers. The security challenges faced by deep learning application to industrial Internet security primarily lie in model training and prediction, and key research directions include interpretability of deep neural networks, cost control of sample collection and calculation, imbalance of sample sets, reliability of model results, tradeoff between availability and security. Furthermore, some suggestion are proposed: a dynamic defense system in depth should be established in terms of overall security strategy; an application-driven and frontier exploration integrated method should be adopted to achieve breakthroughs regarding key technologies; and resources input should be raised for interdisciplinary fields to establish an industry–university–research institute joint research ecosystem. Yang Chen, Ma Ruicheng, Wang Yushi, Zhai Yanlong, Zhu Liehuang《中国工程科学》杂志社articleindustrial Internet security, Internet of Things security, deep learning, data securityEngineering (General). Civil engineering (General)TA1-2040ZH中国工程科学, Vol 23, Iss 2, Pp 95-103 (2021)
institution	DOAJ
collection	DOAJ
language	ZH
topic	industrial Internet security, Internet of Things security, deep learning, data security Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	industrial Internet security, Internet of Things security, deep learning, data security Engineering (General). Civil engineering (General) TA1-2040 Yang Chen, Ma Ruicheng, Wang Yushi, Zhai Yanlong, Zhu Liehuang Deep Learning and Industrial Internet Security: Application and Challenges
description	Industrial Internet security is crucial for strengthening the manufacturing and network sectors of China. Deep learning, owing to its strong expression ability, good adaptability, and high portability, can support the establishment of an industrial Internet security system and method that is intelligent and autonomous. Therefore, it is of great value to promote the integrated innovation of deep learning and industrial Internet security. In this study, we analyze the development demand for industrial Internet security from the perspective of macro industrial environment, security technology, and deep learning system, and summarize the application status of deep learning to industrial Internet security in terms of device, control, network, application, and data layers. The security challenges faced by deep learning application to industrial Internet security primarily lie in model training and prediction, and key research directions include interpretability of deep neural networks, cost control of sample collection and calculation, imbalance of sample sets, reliability of model results, tradeoff between availability and security. Furthermore, some suggestion are proposed: a dynamic defense system in depth should be established in terms of overall security strategy; an application-driven and frontier exploration integrated method should be adopted to achieve breakthroughs regarding key technologies; and resources input should be raised for interdisciplinary fields to establish an industry–university–research institute joint research ecosystem.
format	article
author	Yang Chen, Ma Ruicheng, Wang Yushi, Zhai Yanlong, Zhu Liehuang
author_facet	Yang Chen, Ma Ruicheng, Wang Yushi, Zhai Yanlong, Zhu Liehuang
author_sort	Yang Chen, Ma Ruicheng, Wang Yushi, Zhai Yanlong, Zhu Liehuang
title	Deep Learning and Industrial Internet Security: Application and Challenges
title_short	Deep Learning and Industrial Internet Security: Application and Challenges
title_full	Deep Learning and Industrial Internet Security: Application and Challenges
title_fullStr	Deep Learning and Industrial Internet Security: Application and Challenges
title_full_unstemmed	Deep Learning and Industrial Internet Security: Application and Challenges
title_sort	deep learning and industrial internet security: application and challenges
publisher	《中国工程科学》杂志社
publishDate	2021
url	https://doaj.org/article/3f57a6428ccc4fcb9ba923cc734ff4b8
work_keys_str_mv	AT yangchenmaruichengwangyushizhaiyanlongzhuliehuang deeplearningandindustrialinternetsecurityapplicationandchallenges
_version_	1718407458004664320

Deep Learning and Industrial Internet Security: Application and Challenges

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