Deep Learning for Security and Privacy Preservation in IoT [E-Book] / edited by Aaisha Makkar, Neeraj Kumar.
This book addresses the issues with privacy and security in Internet of things (IoT) networks which are susceptible to cyber-attacks and proposes deep learning-based approaches using artificial neural networks models to achieve a safer and more secured IoT environment. Due to the inadequacy of exist...
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Full text |
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Personal Name(s): | Kumar, Neeraj, editor |
Makkar, Aaisha, editor | |
Edition: |
1st edition 2021. |
Imprint: |
Singapore :
Springer,
2021
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Physical Description: |
XII, 179 pages 58 illustrations, 44 illustrations in color (online resource) |
Note: |
englisch |
ISBN: |
9789811661860 |
DOI: |
10.1007/978-981-16-6186-0 |
Series Title: |
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Signals and Communication Technology
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Subject (LOC): |
- Metamorphosis of Industrial IoT using Deep Leaning
- Deep Learning Models and their Architectures for Computer Vision Applications: A Review
- IoT Data Security with Machine Learning Blockchain: Risks and Countermeasures
- A Review on Cyber Crimes on the Internet of Things
- Deep learning framework for anomaly detection in IoT enabled systems
- Anomaly Detection using Unsupervised Machine Learning Algorithms
- Game Theory Based Privacy Preserving Approach for Collaborative Deep Learning in IoT
- Deep Learning based security preservation of IoT: An industrial machine health monitoring scenario
- Deep learning Models: An Understandable Interpretable Approaches.