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Edge Intelligence
Advanced Deep Transfer Learning for IoT Security
- 1st Edition - January 1, 2026
- Editors: Jawad Ahmad, Shahid Latif, Wadii Boulila, Anis Koubaa, Mujeeb Ur Rehman, Imdad Ullah Khan
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 3 8 2 9 7 - 0
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 3 8 2 9 8 - 7
Edge Intelligence: Advanced Deep Transfer Learning for IoT Security presents a comprehensive exploration into the critical intersection of cybersecurity, edge computing, and deep… Read more
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Request a sales quoteEdge 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.
• 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.
• 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.
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.
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
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
- No. of pages: 250
- Language: English
- Edition: 1
- Published: January 1, 2026
- Imprint: Syngress
- Paperback ISBN: 9780443382970
- eBook ISBN: 9780443382987
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.
Affiliations and expertise
School of Computing Engineering and the Built Environment, Edinburgh Napier UniversitySL
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).
Affiliations and expertise
School of Information Science and Technology, Fudan UniversityWB
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.
Affiliations and expertise
Prince Sultan UniversityAK
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.
Affiliations and expertise
Professor in Computer Science, Advisor to the Rector, and Leader of the Robotics and Internet of Things Research Lab, Prince Sultan University, Saudi ArabiaMR
Mujeeb Ur Rehman
Dr. Mujeeb Ur Rehman holds a position as a Lecturer at De Montfort University's School of Computer Science and Informatics. His research focus during his Ph.D. was on Artificial Intelligence and Cybersecurity. Prior to his current position, Dr. Rehman served in the James Watt School of Engineering at the University of Glasgow, UK. With over ten years of experience, Dr. Rehman is an experienced educator with cutting-edge teaching and research expertise in AI, Machine Learning, Cybersecurity, and IoT, having worked in academic, research, and industry environments. He has taught numerous industry-relevant courses, including Artificial Intelligence, Machine Learning, Neural Networks, AI for Cybersecurity, Programming for Cybersecurity, Cryptography, Computer Systems, and Mathematics for Computing. As a supervisor, Dr. Rehman has overseen several dissertations from PhD, MSc, and undergraduate students, and his contribution to the research community includes co-authoring more than 30 research papers, published in leading international journals (IEEE and IET Transactions) and peer-reviewed international conference proceedings. In recognition of my contributions to the field of Artificial Intelligence and Cybersecurity, Dr. Rehman has been awarded the Global Talent status by the UK Research and Innovation Body and the Royal Academy of Engineering in 2022.
Affiliations and expertise
School of computer Science and Informatics, Cyber Technology Institute, De Montfort UniversityIK
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.Affiliations and expertise
Department of Computer Science, Syed Babar Ali School of Science and Engineering, Lahore University of Management Sciences (LUMS)