@article{1442642,
      recid = {1442642},
      author = {Macintyre, John. and Zhao, Jinghua. and Ma, Xiaomeng.},
      title = {The 2021 International Conference on Machine Learning and  Big Data Analytics for IoT Security and Privacy :  SPIoT-2021., Volume 2 /. SPIoT (Conference)},
      publisher = {Springer,},
      address = {Cham, Switzerland :},
      pages = {1 online resource (999 pages)},
      year = {2022},
      note = {Includes author index.},
      abstract = {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.},
      url = {http://library.usi.edu/record/1442642},
      doi = {https://doi.org/10.1007/978-3-030-89511-2},
}