Book Image

Hands-On Deep Learning for IoT

By : Dr. Mohammad Abdur Razzaque, Md. Rezaul Karim
Book Image

Hands-On Deep Learning for IoT

By: Dr. Mohammad Abdur Razzaque, Md. Rezaul Karim

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.
Table of Contents (15 chapters)
Free Chapter
1
Section 1: IoT Ecosystems, Deep Learning Techniques, and Frameworks
4
Section 2: Hands-On Deep Learning Application Development for IoT
10
Section 3: Advanced Aspects and Analytics in IoT

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...