AI-Driven Next-Generation DDoS Mitigation in IoT: A Comprehensive Survey of Deep Learning and Large Language Models

Authors

  • Mosiur Rahaman Department of Computer Engineering, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand
  • Nicko Cajes Northern Bukidnon State College, Manolo Fortich, Philippines
  • Varsha Arya Electronic Engineering and Computer Science, Hong Kong Metropolitan University, Hong Kong, Center for Interdisciplinary Research, University of Petroleum and Energy Studies (UPES), Dehradun, India, UCRD, Chandigarh University, Chandigarh, India
  • Razaz Waheeb Attar Department of Management, College of Business Administration, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi
  • Kwok Tai Chui Electronic Engineering and Computer Science, Hong Kong Metropolitan University, Hong Kong SAR, China
  • Brij B. Gupta Department of Computer Science and Information Engineering, Asia University, Taichung 413, Taiwan, Department of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan, Symbiosis Centre for Information Technology (SCIT), Symbiosis International University, Pune, India, School of Cybersecurity, Korea University, Seoul, South Korea

DOI:

https://doi.org/10.13052/jwe1540-9589.2571

Keywords:

IoT, Deep Learning, Artificial Intelligence, Language Model

Abstract

The global Internet of Things (IoT) network has experienced unprecedented expansion, reaching 75.44 billion connected devices worldwide. Due to this rapid growth, the IoT ecosystem has become larger but also more vulnerable to various threats in cyberspace. As a result, this situation has accelerated the development of more complex cyberattacks, especially Distributed Denial of Service (DDoS) attacks, which can damage systems by overwhelming network traffic. This attack exploits the inherent vulnerabilities of IoT networks with limited resources, leading to significant service interruptions, monetary losses, and potentially the failure of vital infrastructures. As a result, this survey presents a new taxonomy of attack types, a thorough assessment of conventional and AI-driven detection techniques, and a comparison of advanced countermeasures. This survey aims to provide a comprehensive and systematic overview of DDoS attack detection, prevention, and mitigation approaches for the IoT. A major contribution of this work is the inclusion of blockchain-based security frameworks, federated learning, deep learning, machine learning, and quantum computing paradigms in its discussion for reducing DDoS attacks on the IoT. Furthermore, real-world case studies highlight the severe impact of such attacks on mission-critical domains, including smart cities, healthcare, and industrial control systems (ICSs). This survey provides a basis for cybersecurity researchers, security experts, and hobbyists, grounded in the understanding gained from previous research.

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Author Biographies

Mosiur Rahaman, Department of Computer Engineering, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand

Mosiur Rahaman is a Lecturer in the Department of Computer Engineering at King Mongkut’s University of Technology Thonburi, Thailand. He received his Ph.D. in Computer Science and Information Engineering from Asia University, Taiwan. His research interests include artificial intelligence, machine learning, cybersecurity, the Internet of Things, phishing detection, and digital transformation. He has published extensively in international journals and has served as a postdoctoral researcher, lecturer, assistant professor, editor, reviewer, and conference chair.

Nicko Cajes, Northern Bukidnon State College, Manolo Fortich, Philippines

Nicko Cajes is an Information Technology professional, researcher, and data management practitioner with a strong passion for cybersecurity, artificial intelligence, machine learning, and information systems. He has contributed to internationally published research on phishing and smishing attack detection, as well as the application of real-time machine learning techniques in robotics. His research interests focus on developing innovative and secure computing solutions that address emerging technological challenges. Beyond research, Cajes is actively involved in data management, systems development, and digital transformation initiatives. He has extensive experience in designing and developing web-based information systems, including research and project monitoring platforms, document management systems, and institutional data solutions that enhance operational efficiency and decision-making. Committed to lifelong learning and innovation, he continues to leverage technology to support academic excellence, organizational development, and research advancement through collaboration, integrity, and professional service.

Varsha Arya, Electronic Engineering and Computer Science, Hong Kong Metropolitan University, Hong Kong, Center for Interdisciplinary Research, University of Petroleum and Energy Studies (UPES), Dehradun, India, UCRD, Chandigarh University, Chandigarh, India

Varsha Arya is a researcher affiliated with Hong Kong Metropolitan University (HKMU), Hong Kong. She holds a master’s degree from Rajasthan University, Jaipur, India, and is currently pursuing a Ph.D. at Asia University, Taichung, Taiwan. With over seven years of research experience, she has published more than 25 papers in top journals and conferences. Her research interests span business administration, technology management, cyber–physical systems, cloud computing, healthcare, and networking. She has made significant contributions to deep learning, machine learning, IoT, cybersecurity, and smart city technologies, with a focus on intrusion detection, federated learning, and 6G networks.

Razaz Waheeb Attar, Department of Management, College of Business Administration, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi

Razaz Waheeb Attar is an Assistant Professor of Business Administration within the Department of Management at Princess Nourah bint Abdulrahman University (PNU). Her academic specialization centers heavily on Electronic Commerce (E-Commerce), alongside a dedicated research agenda addressing the evolving dynamics of social commerce, digital learning, and enterprise resource management.

Kwok Tai Chui, Electronic Engineering and Computer Science, Hong Kong Metropolitan University, Hong Kong SAR, China

Kwok Tai Chui received the B.Eng. degree in Electronic and Communication Engineering – Business Intelligence Minor, with first-class honors, and Ph.D. degree in Electronic Engineering from City University of Hong Kong. He was the recipient of international awards in several IEEE events, including 2nd Prize Award (Postgraduate Category) of 2014 IEEE Region 10 Student Paper Contest, and Best Paper Award in IEEE The International Conference on Consumer Electronics-China, in both 2014 and 2015. He has industry experience as a Senior Data Scientist in an Internet of Things (IoT) company. He currently works with the School of Science and Technology at the Hong Kong Metropolitan University as an Associate Professor.

Brij B. Gupta, Department of Computer Science and Information Engineering, Asia University, Taichung 413, Taiwan, Department of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan, Symbiosis Centre for Information Technology (SCIT), Symbiosis International University, Pune, India, School of Cybersecurity, Korea University, Seoul, South Korea

Brij B. Gupta received his Ph.D. degree from the Indian Institute of Technology (IIT) Roorkee, India. With over 19 years of professional experience, he has made significant contributions to the fields of cybersecurity, cloud computing, artificial intelligence, blockchain technologies, and social media networking. His extensive research portfolio includes over 500 publications in esteemed journals and conferences, alongside 35 authored books and 10 patents, accumulating more than 35,000 citations. Currently, Gupta is the Director of the International Center for AI and Cyber Security Research and Innovations at Asia University, Taiwan, where he continues to drive research excellence and innovation in the field of cybersecurity and emerging technologies. His work continues to influence academia and industry, advancing knowledge in cybersecurity and intelligent computing systems. Gupta is a Senior Member of IEEE and ACM.

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2026-10-16

How to Cite

Rahaman, M. ., Cajes, N. ., Arya, V. ., Attar, R. W. ., Chui, K. T. ., & Gupta, B. B. . (2026). AI-Driven Next-Generation DDoS Mitigation in IoT: A Comprehensive Survey of Deep Learning and Large Language Models. Journal of Web Engineering, 25(07), 1215–1294. https://doi.org/10.13052/jwe1540-9589.2571

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2026: Vol 25 Iss 7

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Articles