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Expected by: 01 July 2024
Explainable Artificial Intelligence for Trustworthy Internet of Things

Explainable Artificial Intelligence for Trustworthy Internet of Things

by Mohamed Abdel-Basset, Nour Moustafa, Hossam Hawash, Albert Y. Zomaya

A major challenge for machine learning solutions is that their efficiency in real-world applications is constrained by the current lack of ability of the machine to explain its decisions and activities to human users. Biases based on race, gender, age or location have been a long-standing risk in training AI models. Furthermore, AI model performance can degrade because production data differs from training data.

Explainable AI (XAI) is the practice of interpreting how and why a machine learning algorithm estimates its predictions. It can also help machine learning practitioners and data scientists understand and interpret a model's behaviour. XAI supports end-users to trust a model's auditability and the productive use of AI. It also mitigates AI compliance, legal, security and reputational risks.

Among these applications, the security of IoT infrastructures is vitally essential for improving trust in broad-scale applications such as smart healthcare, smart manufacturing, smart agriculture and smart transportation.

This comprehensive co-authored book offers a complete study of explainable artificial intelligence (XAI) for securing the Internet of things (IoT). The authors present innovative XAI solutions for securing IoT infrastructures against security attacks and privacy threats and cover advanced research topics including responsible security intelligence.

Providing a systematic and thorough overview of the field, this book will be a valuable resource for ICT researchers, AI and data science engineers, security analysts, undergraduate and graduate students and professionals who wish to gain a fundamental understanding of intelligent security solutions.

About the Author

Mohamed Abdel-Basset is an associate professor with the Faculty of Computers and Informatics, Zagazig University, Egypt. His research interests include AI, machine and deep learning, computational intelligence, security intelligence, IoT data mining and applied statistics. He has been program chair for many conferences on AI optimisation and complexity. He is also editor and reviewer for international journals and conferences. He holds a PhD from the Faculty of Computers and Informatics, Menoufia University, Egypt.

Nour Moustafa is a senior lecturer & leader of intelligent security at the School of Engineering & Information Technology, University of New South Wales (UNSW Canberra), Australia. His areas of research interest include cyber security and artificial intelligence, in particular network security, IoT security, intrusion detection and machine and deep learning. He is associate editor of IEEE Systems, IEEE Transactions on Industrial Informatics, IEEE IoT Journal, IEEE Access and Ad Hoc Networks.

Hossam Hawash is a research assistant and assistant lecturer with the Department of Computer Science, Zagazig University, Egypt. His research interests include explainable artificial intelligence, machine and deep learning, the internet of things (IoT), cyber security and fuzzy learning. He holds an MSc from the Department of Computer Science, Faculty of Computers and Informatics, Zagazig University, Egypt.

Albert Y. Zomaya is Peter Nicol Russell Chair Professor of Computing, director of the Centre for Distributed and High-Performance Computing and Australian Research Council professorial fellow in the School of Computer Science, the University of Sydney, Australia. He has published over 30 book titles and is chief editor for the IET Book Series on Big Data. He is a fellow of the AAAS, IEEE and IET.



Item Subjects:
Computing and Networks

Publication Year: 2024

Pages: 472

ISBN-13: 978-1-83953-802-5

Format: HBK

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