The integration of fog computing with the resource-limited Internet of Things (IoT) network formulates the concept of the fog-enabled IoT system. Due to a large number of IoT devices, the IoT is a main source of Big Data. A large volume of sensing data is generated by IoT systems such as smart cities and smart-grid applications. A fundamental research issue is how to provide a fast and efficient data analytics solution for fog-enabled IoT systems. Big Data Analytics in Fog-Enabled IoT Networks: Towards a Privacy and Security Perspective focuses on Big Data analytics in a fog-enabled-IoT system and provides a comprehensive collection of chapters that touch on different issues related to healthcare systems, cyber-threat detection, malware detection, and the security and privacy of IoT Big Data and IoT networks.

This book also emphasizes and facilitates a greater understanding of various security and privacy approaches using advanced artificial intelligence and Big Data technologies such as machine and deep learning, federated learning, blockchain, and edge computing, as well as the countermeasures to overcome the vulnerabilities of the fog-enabled IoT system.

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Yes, you can access Big Data Analytics in Fog-Enabled IoT Networks by Govind P. Gupta, Rakesh Tripathi, Brij B. Gupta, Kwok Tai Chui, Govind P. Gupta,Rakesh Tripathi,Brij B. Gupta,Kwok Tai Chui in PDF and/or ePUB format, as well as other popular books in Computer Science & Computer Networking. We have over one million books available in our catalogue for you to explore.

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Index

Cover Page
Half-Title Page
Title Page
Copyright Page
Contents
Preface
About the Editors
Contributors
Chapter 1 Deep Learning Techniques in Big Data-Enabled Internet-of-Things Devices
Chapter 2 IoMT-based Smart Health Monitoring: The Future of Health Care
Chapter 3 A Review on Intrusion Detection Systems and Cyber Threat Intelligence for Secure IoT-enabled Networks: Challenges and Directions
Chapter 4 Self-adaptive Application Monitoring for Decentralized Edge Frameworks
Chapter 5 Federated Learning and its Application in Malware Detection
Chapter 6 An Ensemble XGBoost Approach for the Detection of Cyber-attacks in the Industrial IoT Domain
Chapter 7 A Review on IoT for the Application of Energy, Environment, and Waste Management: System Architecture and Future Directions
Chapter 8 Analysis of Feature Selection Methods for Android Malware Detection using Machine Learning Techniques
Chapter 9 An Efficient Optimizing Energy Consumption using Modified Bee Colony Optimization in Fog and IoT Networks
Index

About this book

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Table of contents