Advanced Machine Learning for Cyber-Attack Detection in Iot Networks

Erscheinungsjahr 2025
ISBN: 978-0-443-29032-9
Verlag: Elsevier Science

Buch, Englisch, 300 Seiten, Format (B × H): 191 mm x 235 mm

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.

Autoren/Hrsg.

Hoang, Dinh Thai

Nguyen, Diep N.

Hieu, Nguyen Quang

Hossain, Ekram

Fachgebiete

• Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Informationstheorie, Kodierungstheorie
• Mathematik | Informatik EDV | Informatik Technische Informatik Computersicherheit Kryptographie, Datenverschlüsselung
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz

Inhaltsverzeichnis

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

Über Autor(innen)

Hossain, Ekram
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.

Nguyen, Diep N.
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.