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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 one: intelligent host intrusion detection in IoT

Very often, resource-constrained IoT devices become the target for DoS or DDoS attacks by intruders that can make the IoT application unavailable to the consumers. For example, consider an IoT-based remote patient-monitoring system. If the sensor's reading of the patient at a critical time, such as during a heart attack, are not available to their doctors or hospital, the patient may lose their life. In this context, devices or host level intrusion detection is essential for most IoT applications. In use case one, we will consider IoT device or host level intrusion detection.

It is essential to select a good feature or set of features to determine anomalies in IoT devices and networks (such as DoS and DDoS) using predictive methods, including DL. Often, we need time series data for real-time or online anomaly detection...

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