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Record Nr. |
UNINA9910483068103321 |
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Titolo |
The 2020 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy [[electronic resource] ] : SPIoT-2020, Volume 1 / / edited by John MacIntyre, Jinghua Zhao, Xiaomeng Ma |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 |
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ISBN |
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Edizione |
[1st ed. 2021.] |
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Descrizione fisica |
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1 online resource (XXXI, 884 p. 221 illus., 150 illus. in color.) |
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Collana |
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Advances in Intelligent Systems and Computing, , 2194-5365 ; ; 1282 |
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Disciplina |
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Soggetti |
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Engineering—Data processing |
Cooperating objects (Computer systems) |
Computational intelligence |
Machine learning |
Big data |
Data Engineering |
Cyber-Physical Systems |
Computational Intelligence |
Machine Learning |
Big Data |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Sommario/riassunto |
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This book presents the proceedings of The 2020 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy (SPIoT-2020), held in Shanghai, China, on November 6, 2020. Due to the COVID-19 outbreak problem, SPIoT-2020 conference was held online by Tencent Meeting. It provides comprehensive coverage of the latest advances and trends in information technology, science and engineering, addressing a number of broad themes, including novel machine learning and big data analytics methods for IoT security, data mining and statistical modelling for the secure IoT |
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and machine learning-based security detecting protocols, which inspire the development of IoT security and privacy technologies. The contributions cover a wide range of topics: analytics and machine learning applications to IoT security; data-based metrics and risk assessment approaches for IoT; data confidentiality and privacy in IoT; and authentication and access control for data usage in IoT. Outlining promising future research directions, the book is a valuable resource for students, researchers and professionals and provides a useful reference guide for newcomers to the IoT security and privacy field. |
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