Deep learning approaches for security threats in IoT environments / Mohamed Abdel-Basset, Zagazig University, Egypt, Nour Moustafa, UNSW Canberra at the Australian Defence Force Academy, Australia, Hossam Hawash, Zagazig University, Egypt.

Includes bibliographical references and index.

"Deep Learning Approaches for Security Threats in IoT Environments discusses approaches and measures to ensure our IoT systems are secure. This book discusses important concepts of AI and IoT and applies vital approaches that can be used to protect our systems - these include supervised, unsupervised, and semi-supervised Deep Learning approaches as well as Reinforcement and Federated Learning for privacy-preserving. This book applies Digital Forensics to IoT and discusses problems that professionals may encounter when working in the field of IoT forensics, providing ways in which smart devices can solve cyber security issues. Aimed at readers within the cyber security field, this book presents the most recent challenges that are faced in deep learning when creating a secure platform for IoT systems and addresses the possible solutions, paving the way for a more secure future"-- Provided by publisher.

About the Author
Mohamed Abdel-Basset, PhD, is an Associate Professor in the Faculty of Computers and Informatics at Zagazig University, Egypt. He is a Senior Member of the IEEE.

Nour Moustafa, PhD, is a Postgraduate Discipline Coordinator (Cyber) and Senior Lecturer in Cybersecurity and Computing at the School of Engineering and Information Technology at the University of New South Wales, UNSW Canberra, Australia.

Hossam Hawash is an Assistant Lecturer in the Department of Computer Science, Faculty of Computers and Informatics at Zagazig University, Egypt.