Mohamed Abdel-Basset - Deep Learning Techniques for IoT Security and Privacy (Studies in Computational Intelligence, 997)

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Internet of Things (IoT) is a ubiquitous technology for transforming everything we do and the infrastructure which supports us. From smart device to smart cities, companies, critical infrastructure, health care, smart grid, and wearables, which lead to vast amount of daily generated and processed data. This extraordinary scale, ubiquity, and interconnectivity also establish an environment where the confidence and integrity of these IoT applications turn out to be an overriding interest. One only has to look to the headings where attacks on vital infrastructure such as energy generation and distribution, liabilities in our automobiles, and malware in webcams, smartphones, and PCs which we bring into our homes underscore our joint vulnerability. Given the broad attack surfaces being formed and the irregularity between attackers wanting to uncover a particular weakness to take advantage of, whereas defenders should discover and close up all vulnerabilities, IoT generates an unrivaled set of security challenges. During journey of authors, we have had the pleasure of working with a lot of experts in their own fields. This well-timed book will build the readers knowledge about the IoT security challenges, the potential of deep learning to provide remedies for these challenges in the available IoT frameworks, and certain vertical applications. Please join us in the critical mission deep learning-based secure IoT applications.

The topic of this book is deep learning for securing Internet of Things (IoT)which is a subfield of artificial intelligenceconcentrating on the general and challenging security problems in an IoT environment. Intelligence is driven only by learning the inherent representations from the observations acquired in IoT environment. This data-driven learning is incredibly general and may be applied to numerous everyday circumstances from playing games to autonomous driving of vehicles. Thanks to elasticity and generalization, the field of deep learning is evolving very rapidly and attracts lots of interest both from researchers attempting to enhance current or establish new techniques, as well as from specialists fascinated in resolving the cyber-physical security issues in the most effective mode. This book was written as an effort to fulfill the evident gap of workable and structured information about intelligent security solution in IoT environments. On one hand, there are countless research activities all around the world, new research papers are being published almost every day, and a large portion of conferences such as NIPS or ICLR is dedicated to deep learning methods for securing IoT applications. There are many huge research groups concentrating on deep learning solutions methods application in industry, robotics, health care, transportations, and others. The information about the recent research is broadly obtainable but is extremely specialized and abstract to be comprehensible without significant hard work. Even worse is the situation with the practical aspect of deep learning application, as it is not continuously apparent how to make a move from the abstract approach portrayed in the mathematical-heavy way in a research paper to a functioning application to real-world problem. This makes it challenging for somebody concerned in the area to obtain an insightful interpretation of approaches and concepts behind papers and conference talks. There are some very good blog posts about various deep learning aspects illustrated with working examples, but the incomplete presentation of a blog post tolerates the author to discuss only one or two approaches without designing a comprehensive planned picture and screening how diverse approaches are related to each other. This book is our effort to tackle this problem.