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Deep Learning for Security and Privacy Preservation IoT
Barnes and Noble
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Deep Learning for Security and Privacy Preservation IoT in Bloomington, MN
By Barnes & Noble
Current price: $189.00


Deep Learning for Security and Privacy Preservation IoT in Bloomington, MN
Current price: $189.00
Loading Inventory...
Size: EBook
This book addresses the issues with privacy and security in Internet of things (IoT) networks which are susceptible to cyberattacks and proposes deep learningbased approaches using artificial neural networks models to achieve a safer and more secured IoT environment. Due to the inadequacy of existing solutions to cover the entire IoT network security spectrum, the book utilizes artificial neural network models, which are used to classify, recognize, and model complex data including images, voice, and text, to enhance the level of security and privacy of IoT. This is applied to several IoT applications which include wireless sensor networks (WSN), meter reading transmission in smart grid, vehicular ad hoc networks (VANET), industrial IoT and connected networks. The book serves as a reference for researchers, academics, and network engineers who want to develop enhanced security and privacy features in the design of IoT systems.
This book addresses the issues with privacy and security in Internet of things (IoT) networks which are susceptible to cyberattacks and proposes deep learningbased approaches using artificial neural networks models to achieve a safer and more secured IoT environment. Due to the inadequacy of existing solutions to cover the entire IoT network security spectrum, the book utilizes artificial neural network models, which are used to classify, recognize, and model complex data including images, voice, and text, to enhance the level of security and privacy of IoT. This is applied to several IoT applications which include wireless sensor networks (WSN), meter reading transmission in smart grid, vehicular ad hoc networks (VANET), industrial IoT and connected networks. The book serves as a reference for researchers, academics, and network engineers who want to develop enhanced security and privacy features in the design of IoT systems.



















