Big Data Analytics in Fog-Enabled IoT Networks: Towards a Privacy and Security Perspective

by Govind P. Gupta (Editor), Rakesh Tripathi (Editor), Brij B. Gupta (Editor), Kwok Tai Chui (Editor)

Overview

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.

Product Details

ISBN-13:	9781000861860
Publisher:	CRC Press
Publication date:	04/19/2023
Sold by:	Barnes & Noble
Format:	eBook
Pages:	232
File size:	3 MB

About the Author

Govind P. Gupta, Kwok Tai Chui

Table of Contents

1. Deep Learning Techniques in Big Data-Enabled Internet-of-Things Devices. 2. IoMT based Smart Health Monitoring: The Future of HealthCare. 3. A Review on Intrusion Detection Systems and Cyber Threat Intelligence for Secure IoT-Enabled Network: Challenges and Directions. 4. Self-Adaptive Application Monitoring for Decentralized Edge Frameworks. 5. Federated Learning and Its Application in Malware Detection. 6. An Ensemble XGBoost Approach for the Detection of Cyber-Attacks in the Industrial IOT Domain. 7. A Review on IoT for the Application of Energy, Environment, and Waste Management: System Architecture and Future Directions. 8. Analysis of Feature Selection Methods for Android Malware Detection Using Machine Learning Techniques. 9. An Efficient Optimizing Energy Consumption Using Modified Bee Colony Optimization in Fog and IoT Networks.