Self-learning and explainable deep learning network toward the security of artificial intelligence of things

Wu, B; He, S

Self-learning and explainable deep learning network toward the security of artificial intelligence of things

Wu, B He, S

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Publisher:: Springer Nature
Publication Type:: Journal Article
Citation:: Journal of Supercomputing, 2023, 79, (4), pp. 4436-4467
Issue Date:: 2023-03-01

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Field	Value	Language
dc.contributor.author	Wu, B
dc.contributor.author	He, S
dc.date.accessioned	2024-05-27T20:41:29Z
dc.date.available	2024-05-27T20:41:29Z
dc.date.issued	2023-03-01
dc.identifier.citation	Journal of Supercomputing, 2023, 79, (4), pp. 4436-4467
dc.identifier.issn	0920-8542
dc.identifier.issn	1573-0484
dc.identifier.uri	http://hdl.handle.net/10453/179251
dc.description.abstract	At present, the security of the Internet of things (IoT) has aroused great concern in artificial intelligence area, Artificial Intelligence of Things (AIoT) are widely used in various intelligent surveillance scenarios. However, due to the weak interpretability of the model and high data security risks, developing a robust and explainable deep learning network framework for scene understanding under AIoT is extremely difficult. In addition, the fusion of IoT and AI also poses several challenges. To solve these difficulties, we develop a self-learning and explainable deep learning network toward the security of AIoT. The constructed system contains video collection, upload and display as well as data analysis and early warning operation at the embedded device end, and automatically recognizes the behaviors of scene by our developed visual recognition algorithms. In addition, the cloud computing platform can be controlled through our developed network. Our developed visual recognition algorithms contribute to three aspects. First, we propose a lightweight reinforcement learning network model by extracting spatial–temporal feature of different behavior characteristic. Then, we propose a self-paced learning framework through fusing the deep reinforcement learning and transfer learning. Finally, we propose a multi-perspective deep transfer learning model to solve the problem of weak explanation of model. The experimental results show that our proposed model is able to provide high interpretability of model and outperforms the state-of-the-art methods.
dc.language	en
dc.publisher	Springer Nature
dc.relation.ispartof	Journal of Supercomputing
dc.relation.isbasedon	10.1007/s11227-022-04818-4
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0803 Computer Software, 0805 Distributed Computing
dc.subject.classification	Distributed Computing
dc.subject.classification	4606 Distributed computing and systems software
dc.title	Self-learning and explainable deep learning network toward the security of artificial intelligence of things
dc.type	Journal Article
utslib.citation.volume	79
utslib.for	0803 Computer Software
utslib.for	0805 Distributed Computing
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Strength - CRIN - Realtime Information Networks
pubs.organisational-group	University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2024-05-27T20:41:27Z
pubs.issue	4
pubs.publication-status	Published
pubs.volume	79
utslib.citation.issue	4

Abstract:

At present, the security of the Internet of things (IoT) has aroused great concern in artificial intelligence area, Artificial Intelligence of Things (AIoT) are widely used in various intelligent surveillance scenarios. However, due to the weak interpretability of the model and high data security risks, developing a robust and explainable deep learning network framework for scene understanding under AIoT is extremely difficult. In addition, the fusion of IoT and AI also poses several challenges. To solve these difficulties, we develop a self-learning and explainable deep learning network toward the security of AIoT. The constructed system contains video collection, upload and display as well as data analysis and early warning operation at the embedded device end, and automatically recognizes the behaviors of scene by our developed visual recognition algorithms. In addition, the cloud computing platform can be controlled through our developed network. Our developed visual recognition algorithms contribute to three aspects. First, we propose a lightweight reinforcement learning network model by extracting spatial–temporal feature of different behavior characteristic. Then, we propose a self-paced learning framework through fusing the deep reinforcement learning and transfer learning. Finally, we propose a multi-perspective deep transfer learning model to solve the problem of weak explanation of model. The experimental results show that our proposed model is able to provide high interpretability of model and outperforms the state-of-the-art methods.

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http://hdl.handle.net/10453/179251