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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 FREE CHAPTER
2. The End-to-End Life Cycle of the IoT 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

Use case two: traffic-based intelligent network intrusion detection in IoT

Generally, host intrusion (including device level intrusion) exploits outside world communications, and most of the time a successful host intrusion comes with the success of a network intrusion. For example, in botnets, remote command-and-control servers communicate with the compromised machines to give instructions on operations to execute. More importantly, a large number of insecure IoT devices has resulted in a surge of IoT botnet attacks in worldwide IT infrastructure. The Dyn domain name system (DNS) attack in October 2016 is an example of this, wherein the Mirai botnet commanded 100,000 IoT devices to launch the DDoS attack. This incident impacted many popular websites, including GitHub, Amazon, Netflix, Twitter, CNN, and PayPal. In this context, detection of network-level intrusion in IoT is not...

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