The text highlights a comprehensive survey that focuses on all security aspects and challenges facing the Internet of Things systems, including outsourcing techniques for partial computations on edge or cloud while presenting case studies to map security challenges. It further covers three security aspects including Internet of Things device identification and authentication, network traffic intrusion detection, and executable malware files detection.

This book:

Presents a security framework model design named Behavioral Network Traffic Identification and Novelty Anomaly Detection for the IoT Infrastructures

Highlights recent advancements in machine learning, deep learning, and networking standards to boost Internet of Things security

Builds a near real-time solution for identifying Internet of Things devices connecting to a network using their network traffic traces and providing them with sufficient access privileges

Develops a robust framework for detecting IoT anomalous network traffic

Covers an anti-malware solution for detecting malware targeting embedded devices

It will serve as an ideal text for senior undergraduate and graduate students, and professionals in the fields of electrical engineering, electronics and communication engineering, computer engineering, and information technology.

Authors: Salma Abdalla Hamad, Quan Z. Sheng, Wei Emma Zhang