Decision Making and Security Risk Management for IoT Environments

This book contains contemporary research that outlines and addresses security, privacy challenges and decision-making in IoT environments. The authors provide a variety of subjects related to the following Keywords: IoT, security, AI, deep learning, federated learning, intrusion detection systems, and distributed computing paradigms. This book also offers a collection of the most up-to-date research, providing a complete overview of security and privacy-preserving in IoT environments. It introduces new approaches based on machine learning that tackles security challenges and provides the field with new research material that's not covered in the primary literature.
The Internet of Things (IoT) refers to a network of tiny devices linked to the Internet or other communication networks. IoT is gaining popularity, because it opens up new possibilities for developing many modern applications. This would include smart cities, smart agriculture, innovative healthcare services and more. The worldwide IoT market surpassed $100 billion in sales for the first time in 2017, and forecasts show that this number might reach $1.6 trillion by 2025. However, as IoT devices grow more widespread, threats, privacy and security concerns are growing. The massive volume of data exchanged highlights significant challenges to preserving individual privacy and securing shared data. Therefore, securing the IoT environment becomes difficult for research and industry stakeholders.
Researchers, graduate students and educators in the fields of computer science, cybersecurity, distributed systems and artificial intelligence will want to purchase this book. It will also be a valuable companion for users and developers interested in decision-making and security risk management in IoT environments.

A comprehensive review for security and privacy-preserving in IoT and IIoT environments Presents new machine/deep learning-based approaches to secure decision-making in IoT environments Introduces real-world applications of machine/deep/federated learning-based approaches

Auteur

Wadii Boulila received the B.Eng. degree (1st Class Honours with distinction) in computer science from the Aviation School of Borj El Amri, in 2005, the MSc. degree in computer science from the National School of Computer Science (ENSI), University of Manouba, Tunisia, in 2007, and the Ph.D. degree in computer science conjointly from the ENSI and Telecom-Bretagne, University of Rennes 1, France, in 2012. He is currently an associate professor of computer science with Prince Sultan University, Saudi Arabia. He is also a senior researcher with the RIOTU Laboratory, Prince Sultan University, Saudi Arabia, a senior researcher with RIADI Laboratory, University of Manouba, and previously a Senior Research Fellow with the ITI Department, University of Rennes 1, France. Wadii received the award of the young researcher in computer science in Tunisia for the year 2021 from Beit El-Hikma and the award of best researcher from the University of Manouba in Tunisia for the year 2021. He participatedin many research and industrial-funded projects. His primary research interests include data science, big data analytics, deep learning, cybersecurity, artificial intelligence, uncertainty modeling, and image analysis and interpretation. He has served as the chair, a reviewer, and a TPC member for many leading international conferences and journals. His work has gained global recognition, and he has been nominated as one of the top 2% of scientists in his field by Stanford University. Wadii Boulila is an IEEE Senior member, an ACM member, and a Senior Fellow of the Higher Education Academy (SFHEA), U.K.
Jawad Ahmad is an experienced teacher with 10 years of teaching and research experience in prestigious institutes including Edinburgh Napier University (UK), Glasgow Caledonian University (UK), Hongik University (South Korea) and HITEC University Taxila (Pakistan). During his professional career in the UK, South Korea, and Pakistan, he has taught numerous industry-relevant courses such as Programming for Cyber Security, Cryptography, Operating Systems, Computer Systems and Mathematics for Computing. He has supervised several PhD, MSc and undergraduate students in their dissertations. He has 100+ research papers (3000+ Google Citations), including in leading international journals and peer-reviewed international conference proceedings. In 2020, he was recognised as a Global Talent in the area of Cyber Security by the Royal Academy of Engineering (UK). As per clarivate in 2021 and 2022, he was included in the world's top 2% scientists. He has received the best paper awards at two international conferences. He earned a gold medal and bronze medal for best performance in MS (Electrical Engineering) and BS (Electronics Engineering), respectively.
Anis Koubaa is a Full Professor in Computer Science, technology innovator, at Prince Sultan University, where he also serves as the Research and Initiatives Center Director and leads the Robotics and Internet-of-Things Lab. With more than 20 years of experience in research and development, he has driven numerous innovative projects in areas such as data science, unmanned systems, deep learning, robotics, and the Internet of Things. He is a Senior Fellow of the Higher Education Academy of the UK. Prof. Koubaa has received multiple awards, including the AI Leadership Award, the Best AI Product Award, the Winner KAUST Challenge Award, and recognition for his teaching skills. His research interests revolve around developing automated solutions for logistics, using drones and robots for delivery systems. Anis Koubaa has delivered training programs on drones, data science, Python programming, deep learning, and other emerging technologies. Notably, he has authored courses and books on the Robot Operating System (ROS) and the ROSLink protocol, enabling communication between robots, drones, and cloud systems. Koubaa also leads AI projects focusing on real-time face surveillance, vehicle detection, and license plate recognition while developing AI and automation solutions for smart cities and agriculture. His work has gained global recognition, and he has been nominated as one of the top 2% of scientists in his field by Stanford University.

Maha Driss: is a Senior Member of IEEE and a Member of the ACM Professional Chapter. She received her Engineering degree (Hons.) in computer science and her M.Sc. degree from the National School of Computer Science (ENSI) at the University of Manouba, Tunisia, in 2006 and 2007, respectively. In 2011, she received her Ph.D. degree from the University of Rennes 1, France. Dr. Driss has worked in various academic roles throughout her career. From 2012 to 2015, she was an Assistant Professor of computer science at the National Higher Engineering School of Tunis at the University of Tunis, Tunisia. From 2015 to 2021, she worked as an Assistant Professor of computer science at the Information System Department, College of Computer Science and Engineering at Taibah University, Saudi Arabia. From 2021 to 2022, she was a Senior Researcher at the Security Engineering Lab at Prince Sultan University, Saudi Arabia. Currently, she is an Assistant Professor in the Computer Science department at the College of Computer and Information Sciences at Prince Sultan University, Saudi Arabia. Additionally, she is a Senior Researcher with the RIADI Laboratory at the University of Manouba. Dr. Driss' research interests focus on several areas, including software engineering, service computing, distributed systems, IoT and IIoT, artificial intelligence, and security engineering. She has served in various leadership positions, including Guest Editor, Chair, Reviewer, and TPC Member for many leading international conferences and journals. She has published numerous articles in reputable international journals and conferences. Moreover, Dr. Driss has obtaine…