Advanced Machine Learning for Cyber-Attack Detection in IoT Networks
- 1st Edition - June 1, 2025
- Editors: Dinh Thai Hoang, Nguyen Quang Hieu, Diep N. Nguyen, Ekram Hossain
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 2 9 0 3 2 - 9
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 2 9 0 3 3 - 6
Advanced Machine Learning for Cyber-Attack Detection in IoT Networks analyzes diverse machine learning techniques, including supervised, unsupervised, reinforcement, and deep l… Read more
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Request a sales quote- Presents a comprehensive overview of research on IoT security threats and potential attacks
- Investigates machine learning techniques, their mathematical foundations, and their application in cybersecurity
- Presents metrics for evaluating the performance of machine learning models as well as benchmark datasets and evaluation frameworks for assessing IoT systems
2. Evaluation and Performance Metrics for IoT Security Networks
3. Adversarial Machine Learning Techniques for the Industrial IoT Paradigm
4. Federated Learning for Distributed Intrusion Detection in IoT Networks
5. Safeguarding IoT Networks with Generative Adversarial Networks
6. Meta-Learning for Cyber-Attack Detection in IoT Networks
7. Transfer Learning with CNN for Cyberattack Detection in IoT Networks
8. Lightweight Intrusion Detection Methods Based on Artificial Intelligence for IoT Networks
9. A New Federated Learning System with Attention-Aware Aggregation Method for Intrusion Detection Systems
10. Enhancing Intrusion Detection using Improved Sparrow Search Algorithm with Deep Learning on Internet of Things Environment
11. Advancing Cyberattack Detection for In-Vehicle Network: A Comparative Study of Machine Learning-based Intrusion Detection System
12. Practical Approaches Towards IoT Dataset Generation for Security Experiments
13. Challenges and Potential Research Directions for Machine Learning-based Cyber-Attack Detection in IoT Networks
- No. of pages: 300
- Language: English
- Edition: 1
- Published: June 1, 2025
- Imprint: Academic Press
- Paperback ISBN: 9780443290329
- eBook ISBN: 9780443290336
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Dinh Thai Hoang
Dinh Thai Hoang (M’16, SM’22) is currently a faculty member at the School of Electrical and Data Engineering, University of Technology Sydney, Australia. He received his Ph.D. in Computer Science and Engineering from the Nanyang Technological University, Singapore 2016. His research interests include emerging wireless communications and networking topics, especially machine learning applications in networking, edge computing, and cybersecurity. He has received several precious awards, including the Australian Research Council Discovery Early Career Researcher Award, IEEE TCSC Award for Excellence in Scalable Computing for Contributions on “Intelligent Mobile Edge Computing Systems” (Early Career Researcher), IEEE Asia-Pacific Board (APB) Outstanding Paper Award 2022, and IEEE Communications Society Best Survey Paper Award 2023. He is currently an Editor of IEEE TMC, IEEE TWC, and IEEE COMST.
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Nguyen Quang Hieu
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Diep N. Nguyen
EH
Ekram Hossain
Ekram Hossain (Fellow, IEEE) is a Professor and the Associate Head (Graduate Studies) of the Department of Electrical and Computer Engineering, University of Manitoba, Canada. He is a Member (Class of 2016) of the College of the Royal Society of Canada. He is also a Fellow of the Canadian Academy of Engineering and the Engineering Institute of Canada. His current research interests include design, analysis, and optimization of next-generation (xG) cellular wireless networks, applied machine learning, and communication network economics. He was elevated to an IEEE fellow, for contributions to spectrum management and resource allocation in cognitive and cellular radio networks. He was listed as a Clarivate Analytics Highly Cited Researcher in Computer Science in 2017-2023. He has won several research awards, including the 2017 IEEE Communications Society (ComSoc) Best Survey Paper Award and the 2011 IEEE Communications Society Fred Ellersick Prize Paper Award. He was a Distinguished Lecturer of the IEEE Communications Society and the IEEE Vehicular Technology Society.