CURRENTLY SOLD OUT
About this product
Product Identifiers
PublisherTaylor & Francis Group
ISBN-101032206446
ISBN-139781032206448
eBay Product ID (ePID)6058381411
Product Key Features
Number of Pages264 Pages
LanguageEnglish
Publication NameBig Data Analytics in Fog-Enabled Iot Networks : Towards a Privacy and Security Perspective
SubjectEngineering (General), Security / Networking, Data Processing
Publication Year2023
TypeTextbook
Subject AreaComputers, Technology & Engineering
AuthorRakesh Tripathi
FormatHardcover
Dimensions
Item Length9.2 in
Item Width6.1 in
Additional Product Features
LCCN2022-049027
Dewey Edition23/eng/20230103
IllustratedYes
Dewey Decimal005.8
Table Of Content1. 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 System 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 Cyberattacks in the Industrial IOT Domain. 7. A review on IoT for the application of energy, environment, and waste management: system architecture and future direction. 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.
SynopsisIntegration of Fog computing with the resource limited IoT network, formulate the concept of Fog-enabled IoT system. Due to large number of deployments of IoT devices, a IoT is a main source of Big data and a very high volume of sensing data is generated by IoT system such as smart cities and smart grid applications. To provide a fast and efficient data analytics solution for Fog-enabled IoT system is a very fundamental research issue. This book focus on Big data Analytics in Fog-enabled-IoT system and provides a comprehensive collection of chapters that touches different issues related to Healthcare system, Cyber threat detection, Malware detection, security and privacy of big IoT data and IoT network. 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., 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., 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.
LC Classification NumberTK5105.8857.B54 2023
