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Advanced Machine Learning for Cyber-Attack Detection in IoT Networks

  • 1st Edition - May 12, 2025
  • Latest edition
  • Editors: Dinh Thai Hoang, Nguyen Quang Hieu, Diep N. Nguyen, Ekram Hossain
  • Language: English

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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Description

Advanced Machine Learning for Cyber-Attack Detection in IoT Networks analyzes diverse machine learning techniques, including supervised, unsupervised, reinforcement, and deep learning, along with their applications in detecting and preventing cyberattacks in future IoT systems. Chapters investigate the key challenges and vulnerabilities found in IoT security, how to handle challenges in data collection and pre-processing specific to IoT environments, as well as what metrics to consider for evaluating the performance of machine learning models. Other sections look at the training, validation, and evaluation of supervised learning models and present case studies and examples that demonstrate the application of supervised learning in IoT security.

Key features

  • 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

Readership

Graduate students, researchers, and professional engineers in the fields of IoT network development and analysis, cybersecurity, and machine learning applications

Table of contents

1. Machine Learning for Cyber-Attack Detection in IoT Networks: An Overview

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

Product details

  • Edition: 1
  • Latest edition
  • Published: May 28, 2025
  • Language: English

About the editors

DH

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.

Affiliations and expertise
University of Technology Sydney, Australia

NH

Nguyen Quang Hieu

Nguyen Quang Hieu received a B.E. degree from Hanoi University of Science Technology, Vietnam, in 2018. He is currently a Ph.D. student at the School of Electrical and Data Engineering, University of Technology (UTS), Sydney, Australia. Before joining UTS, he was a research assistant at the School of Computer Science and Engineering, Nanyang Technological University, Singapore. His research interests include wireless communications and machine learning. He has received several awards, including the IEEE ComSoc Student Travel Grant at ICC in Rome, Italy (2023), the Outstanding Higher Degree Research Student Award from the School of Electrical and Data Engineering at UTS (2023), an Honorary Mention in the IEEE ComSoc Student Competition “Communications Technology Changing the World” (2023), and he served as the Vice-Chair of the IEEE Student Branch at UTS, New South Wales, Australia (2023).
Affiliations and expertise
University of Technology Sydney, Australia

DN

Diep N. Nguyen

Diep N. Nguyen received the M.E. degree in electrical and computer engineering from the University of California at San Diego (UCSD), La Jolla, CA, USA, in 2008, and the Ph.D. degree in electrical and computer engineering from The University of Arizona (UA), Tucson, AZ, USA, in 2013. He is currently the Head of 5G/6G Wireless Communications and Networking Lab, Director of Agile Communications and Computing group, University of Technology Sydney (UTS), Sydney, NSW, Australia. Before joining UTS, he was a DECRA Research Fellow with Macquarie University, Australia, and a Member of the Technical Staff with Broadcom Corporation, CA, USA, and ARCON Corporation, Boston, USA, consulting the Federal Administration of Aviation, USA, on turning detection of UAVs and aircraft, and the U.S. Air Force Research Laboratory on anti-jamming. His research interests include computer networking, wireless communications, and machine learning application, with emphasis on systems’ performance and security/privacy. Dr. Nguyen received several awards from LG Electronics, UCSD, UA, the U.S. National Science Foundation, and the Australian Research Council.
Affiliations and expertise
University of Technology Sydney, Australia

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.

Affiliations and expertise
University of Manitoba, Canada

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