Decision Making and Security Risk Management for IoT Environments

Abstract

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.

Citation

Boulila, W., Ahmad, J., Koubaa, A., Driss, M., & Farah, I. R. (Eds.). (2024). Decision Making and Security Risk Management for IoT Environments. Springer. https://doi.org/10.1007/978-3-031-47590-0

• Book Type: Edited Book
• Online Publication Date: Feb 21, 2024
• Publication Date: Feb 22, 2024
• Deposit Date: May 2, 2024
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Series Title: Advances in Information Security
• Series ISSN: 1568-2633
• Book Title: Advances in Information Security
• ISBN: 978-3-031-47589-4
• DOI: https://doi.org/10.1007/978-3-031-47590-0