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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 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.
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 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.
This book presents the proceedings of the 2020 2nd International
Conference on Machine Learning and Big Data Analytics for IoT
Security and Privacy (SPIoT-2021), online conference, on 30 October
2021. 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 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.
This book presents the proceedings of the 2020 2nd International
Conference on Machine Learning and Big Data Analytics for IoT
Security and Privacy (SPIoT-2021), online conference, on 30 October
2021. 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 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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