Overview
- Presents proceedings of the 2021 International Conference on Machine Learning and Big Data Analytics
- Contains the state-of-the-art in IoT security and privacy
- Written by experts in the field
Part of the book series: Lecture Notes on Data Engineering and Communications Technologies (LNDECT, volume 97)
Included in the following conference series:
Conference proceedings info: SPIoT 2021.
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Table of contents (150 papers)
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Front Matter
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Novel Machine Learning Methods for IoT Security
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Front Matter
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Other volumes
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The 2021 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy
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The 2021 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy
Keywords
- Novel Machine Learning and Big Data Analytics Methods
- Data Mining and Statistical Modelling for the Secure IoT
- Machine Learning Based Security Detecting Protocols
- Machine Learning Experiments, Test-beds and Prototyping Systems
- Analytics and Machine Learning Applications to IoT Security
- Data Based Metrics and Risk Assessment Approaches for IoT
- Data Confidentiality and Privacy in IoT
- Authentication and Access Control for Data Usage in IoT
- Data-driven Co-design of Communication
- Big Data Analytics/Deep Learning Edge/Fog Security
- Emerging Standards for IoT Security
- Big Data Analytics
- Smart City
- Cyber-Physical System
- Machine Learning
About this book
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.
Editors and Affiliations
Bibliographic Information
Book Title: The 2021 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy
Book Subtitle: SPIoT-2021 Volume 1
Editors: John Macintyre, Jinghua Zhao, Xiaomeng Ma
Series Title: Lecture Notes on Data Engineering and Communications Technologies
DOI: https://doi.org/10.1007/978-3-030-89508-2
Publisher: Springer Cham
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
Softcover ISBN: 978-3-030-89507-5Published: 30 October 2021
eBook ISBN: 978-3-030-89508-2Published: 27 October 2021
Series ISSN: 2367-4512
Series E-ISSN: 2367-4520
Edition Number: 1
Number of Pages: XXI, 1154
Number of Illustrations: 147 b/w illustrations, 190 illustrations in colour
Topics: Data Engineering, Cyber-physical systems, IoT, Computational Intelligence, Big Data, Artificial Intelligence