Information from the 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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This record has an Impact Signal of 72/100 based on recency, source, collaboration, and bibliographic signals. It prioritizes monitoring and is not a judgment of research quality.
Related topics: Network Security and Intrusion Detection · Internet of Things and AI · Internet Traffic Analysis and Secure E-voting
Thai researcher and institutional participation
Mosiur Rahaman · King Mongkut's University of Technology Thonburi
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