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Title	Iot Traffic-Based Ddos Attacks Detection Mechanisms: A Comprehensive Review
ID_Doc	33934
Authors	Shukla P.; Krishna C.R.; Patil N.V.
Year	2024
Published	Journal of Supercomputing, 80, 7
DOI	http://dx.doi.org/10.1007/s11227-023-05843-7
Abstract	The Internet of Things (IoT) has emerged as an inevitable part of human life, that includes online learning, smart homes, smart cars, smart grids, smart cities, agriculture, and e-healthcare. It allows us to operate them 24/7 from anywhere. These smart IoT devices streamline our daily lives by automating everything around us. Several security issues have arisen with the continuous growth of non-secure IoT devices. Distributed Denial of Service (DDoS) attack is one of the most prominent security threats to Internet-based services and IoT platforms. It has the potential to break down the victim’s server or network by transferring an immense amount of irrelevant traffic from the pool of compromised IoT devices. In this article, we present: (i) A comprehensive cyberattacks taxonomy for IoT platforms, (ii) Systematically demonstrate IoT technology: evolution, applications, and challenges, (iv) Systematic review of existing machine learning (ML) and deep learning (DL)-based detection approaches for large-scale IoT traffic-based DDoS attacks, (v) Characterize publicly available IoT-traffic-specific datasets, and (vi) Discuss various open research issues with possible solutions for detecting IoT traffic-based DDoS attacks, including future directions. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.
Author Keywords	Cyberattacks taxonomy; Deep learning; Distributed denial of service (DDoS) attacks; Internet of Things (IoT); IoT platforms; Machine learning

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