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Hands-On Deep Learning for IoT

You're reading from   Hands-On Deep Learning for IoT Train neural network models to develop intelligent IoT applications

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Product type Paperback
Published in Jun 2019
Publisher Packt
ISBN-13 9781789616132
Length 308 pages
Edition 1st Edition
Languages
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Authors (3):
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Aditya Trivedi Aditya Trivedi
Author Profile Icon Aditya Trivedi
Aditya Trivedi
Md. Rezaul Karim Md. Rezaul Karim
Author Profile Icon Md. Rezaul Karim
Md. Rezaul Karim
Dr. Mohammad Abdur Razzaque Dr. Mohammad Abdur Razzaque
Author Profile Icon Dr. Mohammad Abdur Razzaque
Dr. Mohammad Abdur Razzaque
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Toc

Table of Contents (15) Chapters Close

Preface 1. Section 1: IoT Ecosystems, Deep Learning Techniques, and Frameworks
2. The End-to-End Life Cycle of the IoT FREE CHAPTER 3. Deep Learning Architectures for IoT 4. Section 2: Hands-On Deep Learning Application Development for IoT
5. Image Recognition in IoT 6. Audio/Speech/Voice Recognition in IoT 7. Indoor Localization in IoT 8. Physiological and Psychological State Detection in IoT 9. IoT Security 10. Section 3: Advanced Aspects and Analytics in IoT
11. Predictive Maintenance for IoT 12. Deep Learning in Healthcare IoT 13. What's Next - Wrapping Up and Future Directions 14. Other Books You May Enjoy

Summary

Security is the single most important issue in IoT realization. Traditional PC/desktop security solutions, especially signature-based solutions, are not effective in IoT applications. Behavior-based solutions, such as anomaly detection, are preferable in IoT. DL/ML models are very useful tools in IoT for data analysis and also for security incident detection. In this chapter, we presented DL-based network and device behavioral data analysis, and security incident detection techniques for IoT applications in general. In the first part of this chapter, we briefly described various IoT security attacks and their potential detection techniques, including DL/ML based techniques. We considered two different levels of intrusion detection in IoT applications. The first use case is on device-level or host-level intrusion detection, and the second use case is on network-level intrusion...

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