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...
Hands-On Deep Learning for IoT
By :
Hands-On Deep Learning for IoT
By:
Overview of this book
Artificial Intelligence is growing quickly, which is driven by advancements in neural networks(NN) and deep learning (DL). With an increase in investments in smart cities, smart healthcare, and industrial Internet of Things (IoT), commercialization of IoT will soon be at peak in which massive amounts of data generated by IoT devices need to be processed at scale.
Hands-On Deep Learning for IoT will provide deeper insights into IoT data, which will start by introducing how DL fits into the context of making IoT applications smarter. It then covers how to build deep architectures using TensorFlow, Keras, and Chainer for IoT.
You’ll learn how to train convolutional neural networks(CNN) to develop applications for image-based road faults detection and smart garbage separation, followed by implementing voice-initiated smart light control and home access mechanisms powered by recurrent neural networks(RNN).
You’ll master IoT applications for indoor localization, predictive maintenance, and locating equipment in a large hospital using autoencoders, DeepFi, and LSTM networks. Furthermore, you’ll learn IoT application development for healthcare with IoT security enhanced.
By the end of this book, you will have sufficient knowledge need to use deep learning efficiently to power your IoT-based applications for smarter decision making.
Related Content you might be interested in
Current Title:
Hands-On Deep Learning for IoT
Table of Contents (15 chapters)
Preface
Free Chapter
Section 1: IoT Ecosystems, Deep Learning Techniques, and Frameworks
The End-to-End Life Cycle of the IoT
Deep Learning Architectures for IoT
Section 2: Hands-On Deep Learning Application Development for IoT
Image Recognition in IoT
Audio/Speech/Voice Recognition in IoT
Indoor Localization in IoT
Physiological and Psychological State Detection in IoT
IoT Security
Section 3: Advanced Aspects and Analytics in IoT
Predictive Maintenance for IoT
Deep Learning in Healthcare IoT
What's Next - Wrapping Up and Future Directions
Other Books You May Enjoy
Customer Reviews