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Springer SPIoT-2021 Machine Learning IoT Security Conference Proceedings

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Description

This Springer conference proceedings volume captures the latest research presented at the 2nd International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy, held online on 30 October 2021. The collection brings together cutting-edge work on machine learning and big data analytics methods specifically designed to strengthen IoT security infrastructure. Featuring contributions on data mining, statistical modelling, and machine learning-based security detection protocols, this volume addresses critical challenges in protecting internet-connected devices and systems. Ideal for researchers, engineers, and practitioners working at the intersection of machine learning, big data analytics, and IoT security, the proceedings offer practical insights into the latest methodologies and frameworks for developing secure IoT environments. Volume 1 of this series provides a comprehensive overview of current trends and advances shaping the future of IoT security technology.

Key Features

SPIoT-2021 proceedings documents research from the 2nd International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy held in October 2021.

Covers novel machine learning and big data analytics methods specifically developed for enhancing IoT security across diverse applications and industries.

Includes peer-reviewed contributions on data mining and statistical modelling techniques that strengthen secure IoT architecture and implementation.

Features machine learning-based security detection protocols and frameworks designed to identify and prevent emerging threats in connected device networks.

Published by Springer as part of the Lecture Notes on Data Engineering and Communications Technologies series, Volume 97, ensuring academic rigour and technical credibility.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
30 October 2021
Listed Since
17 September 2021

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