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 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...