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

Big Data Analytics in Fog-Enabled IoT Networks

Towards a Privacy and Security Perspective
 
Kiadás sorszáma: 1
Kiadó: CRC Press
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A termék adatai:
ISBN13:9781032206448
ISBN10:1032206446
Kötéstípus:Keménykötés
Terjedelem:232 oldal
Méret:234x156 mm
Súly:590 g
Nyelv:angol
Illusztrációk: 44 Illustrations, black & white; 4 Halftones, black & white; 40 Line drawings, black & white; 45 Tables, black & white
605
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Rövid leírás:

This book emphasizes and facilitate a greater understanding of various security and privacy approaches using the advance AI and Big data technologies like machine/deep learning, federated learning, blockchain, edge computing and the countermeasures to overcome the vulnerabilities of the Fog-enabled IoT system.

Hosszú leírás:

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.

Tartalomjegyzék:

    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.