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Edge Intelligence

Advanced Deep Transfer Learning for IoT Security

  • 1st book:metaData.edition - January 16, 2026
  • book:metaData.latestEdition
  • common:contributors.editors Jawad Ahmad, Shahid Latif, Wadii Boulila, Anis Koubaa, Mujeeb Ur Rehman, Imdad Ullah Khan
  • publicationLanguages:language

Edge Intelligence: Advanced Deep Transfer Learning for IoT Security presents a comprehensive exploration into the critical intersection of cybersecurity, edge computing, and deep l… seeMoreDescription

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Edge Intelligence: Advanced Deep Transfer Learning for IoT Security presents a comprehensive exploration into the critical intersection of cybersecurity, edge computing, and deep learning, offering practitioners, researchers, and cybersecurity professionals a definitive guide to protect IoT/IIoT systems. This book delves into the synergistic potential of edge computing and advanced machine/deep learning algorithms, providing insights into lightweight and resource-efficient models with a special focus on resource-constrained edge devices. The rapidly evolving nature of cyberattacks underscores the need for updated and integrated resources that address the intersection of cybersecurity, edge computing, and deep learning. The authors address this issue by offering practical insights, lightweight models, and proactive defense mechanisms tailored to the unique challenges of securing edge devices and networks. This book is not only written to provide its audience effective strategies to detect and mitigate network intrusions by leveraging edge intelligence and advanced deep transfer learning techniques but also to provide practical insights and implementation guidelines tailored to resource-constrained edge devices.

promoMetaData.keyFeatures

  • Examines the potential of edge computing and deep transfer learning, offering in-depth insights into how edge intelligence can be leveraged to enhance IoT and IIoT security
  • Emphasizes the development of lightweight and resource-efficient models suitable for deployment on edge devices, ensuring that security measures can be effectively implemented without imposing undue computational burden or network overhead
  • Presents practical examples, case studies, and implementation guidelines that demonstrate how advanced deep transfer learning techniques can be applied to address real-world security challenges in IoT and IIoT deployments

promoMetaData.readership

Information security professionals, cybercrime and digital forensic investigators, cyber response and remediation teams, forensic analysts, software developers, e-discovery researchers, security managers, Computer Science analysts, consultants, and researchers in academia and industry. This book will also be useful to professionals, researchers, academics, students and practitioners in the field of computer science and cybersecurity focused on IoT security, deep learning, edge computing, and Industrial Internet of Things (IIoT), specifically those involved in security-related aspects

promoMetaData.tableOfContents

Chapter 1. Introduction to IoT and IIoT Security

1.1. Overview of IoT/IIoT

1.2. Current security

1.3. Basics of Edge Computing

Chapter 2. Fundamentals of Deep Learning and Transfer Learning

2.1. Deep learning concepts

2.2. Transfer learning principles

Chapter 3. Edge Computing: Architecture and Security

3.1. Secure Architecture Design

3.2. Security protocols at the edge

3.3. Case studies of edge security implementations

Chapter 4. Deep Transfer Learning for Intrusion and Anomaly Detection

4.1. Intrusion detection systems (IDS)

4.2. Application of deep transfer learning in IDS

4.3. Anomaly detection using deep transfer learning

Chapter 5. Resource-Efficient Models for Edge Devices

5.1. Challenges and Strategies

5.2. Designing lightweight models

5.3. Optimization techniques for resource-constrained environments

5.4. Real-world applications and case studies

5.5. Adaptation for Edge Devices

Chapter 6. Secure Communication and Privacy-Preserving Techniques in Edge Intelligence

6.1. Secure Communication

6.2. Privacy concerns in IoT/IIoT

6.3. Balancing security and privacy

Chapter 7. Case Studies and Industry Applications

7.1. Analysis of industry-specific applications

7.2. Case Studies of Implementation

7.3. Impact on Business and Society

Chapter 8. Future Trends and Emerging Technologies in IoT Security

8.1. Predictions for the future of IoT/IIoT security

8.2. Emerging technologies in edge intelligence

8.3. Predictions and Upcoming Challenges

8.4. Preparing for the next wave of cybersecurity challenges

Chapter 9. Developing and Implementing a Comprehensive IoT Security Strategy

9.1. Assessment and Risk Management

9.2. Developing Adaptive Security Policies

9.3. Implementing Edge Intelligence in IoT/IIoT

9.4. Importance of Skilling and Training

9.5. Resources and Programs

Chapter 10. Conclusion

10.1. Review of Core Concepts

10.2. Strategic Insights

10.3. Technological Advancements

10.4. Evolving Security Paradigms

promoMetaData.productDetails

  • productDetails.edition: 1
  • book:metaData.latestEdition
  • productDetails.published: January 20, 2026
  • publicationLanguages:languageTitle: publicationLanguages:en

promoMetaData.aboutTheEditors

JA

Jawad Ahmad

Dr. Jawad Ahmad (SMIEEE) is a highly experienced teacher with a decade of teaching and research experience in prestigious institutes. He has taught at renowned institutions such as Edinburgh Napier University (UK) and Glasgow Caledonian University (UK) etc. He has also served as a supervisor for several PhD, MSc, and undergraduate students, providing guidance and support for their dissertations. He has published in renowned journals including IEEE Transactions, ACM Transactions, Elsevier and Springer with over 150 research papers and 4500 citations. For the past three years, his name has appeared on the list of the world's top 2% scientists in Cybersecurity, as published by Clarivate (a list endorsed by Stanford University, USA). Furthermore, in 2020, he was recognized as a Global Talent in the area of Cybersecurity by the Royal Academy of Engineering (UK). To date, he has secured research and funding grants totalling £195K. In terms of academic achievements, he has earned a Gold medal for his outstanding performance in MSc and a Bronze medal for his achievements in BSc.

promoMetaData.affiliationsAndExpertise
School of Computing Engineering and the Built Environment, Edinburgh Napier University, UK

SL

Shahid Latif

Shahid Latif received the B.Sc. and M.Sc. degrees in electrical engineering from HITEC University Taxila, Pakistan, in 2013 and 2018, respectively. He is currently pursuing the Ph.D. degree with the School of Information Science and Engineering, Fudan University, Shanghai, China. From 2015 to 2019, he served as a Lecturer with the Department of Electrical Engineering, HITEC University Taxila. During his Teaching Carrier, he has supervised several projects in the field of electronics, embedded systems, control systems, and the Internet of Things. He is currently working in the research area of cybersecurity of the Industrial Internet of Things (IIoT).

promoMetaData.affiliationsAndExpertise
School of Information Science and Technology, Fudan University, China

WB

Wadii Boulila

Dr. Wadii Boulila received the B.Eng. degree (Hons.) in computer science from the Aviation School of Borj El Amri, in 2005, the M.Sc. 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 jointly from 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; and the RIADI Laboratory, University of Manouba. Previously, he was a Senior Research Fellow with the ITI Department, University of Rennes 1. He has participated in numerous research and industrial-funded projects. His primary research interests include data science, computer vision, big data analytics, deep learning, cybersecurity, artificial intelligence, and uncertainty modeling. He is an ACM Member and a Senior Fellow of the Higher Education Academy (SFHEA), U.K. He received the Award of the Young Researcher in computer science in Tunisia for the year 2021 from Beit El-Hikma; the Award of Best Researcher from the University of Manouba, in 2021; and the Award of Most Cited Researcher at the University of Manouba, in 2022. He has served as the chair, a reviewer, and a TPC member for many leading international conferences and journals.

promoMetaData.affiliationsAndExpertise
Prince Sultan University, Saudi Arabia

AK

Anis Koubaa

Anis Koubaa is a Professor in Computer Science, Advisor to the Rector, and Leader of the Robotics and Internet of Things Research Lab, in Prince Sultan University. He is also R&D Consultant at Gaitech Robotics in China and Senior Researcher in CISTER/INESC TEC and ISEP-IPP, Porto, Portugal. He has been the Chair of the ACM Chapter in Saudi Arabia since 2014. He is also a Senior Fellow of the Higher Education Academy (HEA) in UK. He received several distinctions and awards including the Rector Research Award in 2010 at Al-Imam Mohamed bin Saud University, and the Rector Teaching Award in 2016 at Prince Sultan University. He is the Editor in Chief of the Robotics Software Engineering topic of the International Journal of Advanced Robotics Systems, Associate Editor in the Cyber-Physical Journal (Taylor & Francis). He is also the authors of six books with Springer on robots, sensor networks and Robot Operating Systems (ROS). He has been also actively participating in program committees of several international conferences including, ACM/IEEE International Conference on Cyber-Physical Systems, International Conference on Robotics Computing, European Conference on Wireless Sensor Networks, IEEE International Conference on Autonomous Robot Systems and Competitions, IEEE International Workshop on Factory Communication Systems. He is the author of more than 200 journal and conference publications, and one patent. He received several research grants as principal investigator, and he established research collaboration between Prince Sultan University and Gaitech Robotics for the development of robots and drones, and ROS.
promoMetaData.affiliationsAndExpertise
Professor in Computer Science, Advisor to the Rector, and Leader of the Robotics and Internet of Things Research Lab, Prince Sultan University, Saudi Arabia

MR

Mujeeb Ur Rehman

Dr Mujeeb Ur Rehman is an experienced academic with more than 10 years of teaching and research experience at prestigious institutions, including De Montfort University (UK), University of Glasgow (UK), and York St John University (UK), among others. He is recognised in the Stanford/Elsevier “World’s Top 2% Scientists” (2025). He is a Senior Fellow of the Higher Education Academy (SFHEA), a Senior Member of IEEE, and a Professional Member of BCS, he actively contributes to the academic and research community. He has supervised numerous PhD, MSc, and undergraduate students in their dissertations and has published over 50 research papers in leading international journals such as IEEE Transactions, IET Transactions, and Elsevier, as well as in peer-reviewed international conference proceedings. In 2022, he was endorsed as a Global Talent in Artificial Intelligence and Cyber Security by the Royal Academy of Engineering (UK). He has also received several best paper awards at international conferences. Academically, he earned a Gold Medal in his MS (Engineering) degree and a Distinction in his BS (Engineering) degree. In addition to his academic achievements, he is a member of the Talent Peer Review College at UKRI, and the National Institute for Health and Care Research (NIHR), where he evaluates funding applications and contributes to shaping research directions. Furthermore, he has successfully led and collaborated on multiple internationally funded research projects, securing over £1.5 million in competitive grants both within the UK and internationally.

promoMetaData.affiliationsAndExpertise
School of Computer Science and Informatics, Cyber Technology Institute, De Montfort University, UK

IK

Imdad Ullah Khan

Dr. Imdad Ullah Khan is an Associate Professor of Computer Science at LUMS School of Science and Engineering, Pakistan. He completed his Ph.D. in Computer Science from Rutgers, the State University of New Jersey, where he had the good fortune of being advised by Endre Szemerédi.

Dr. Khan also directs the Data Analysis Lab at LUMS. His research focuses on Algorithms, Graph and Social Network Analysis, Data Science, Machine Learning, and Bioinformatics.
promoMetaData.affiliationsAndExpertise
Department of Computer Science, Syed Babar Ali School of Science and Engineering, Lahore University of Management Sciences (LUMS), Pakistan

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