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Title	A Comprehensive Review Of Recent Types Of Flooding Attack And Defense Methods In Iot-Based Smart Environments
ID_Doc	929
Authors	Khalaf B.A.; Othman S.H.; Razak S.A.; Konios A.
Year	2024
Published	Journal of Soft Computing and Data Mining, 5, 2
DOI	http://dx.doi.org/10.30880/jscdm.2024.05.02.009
Abstract	In an attempt to completely transform people's lives, smart cities have implemented a collection of remodelings. Nevertheless, even though smart cities greatly enrich people's quality of life and provide significant convenience, there are still more unaddressed cyber security risks, such as malicious cyberattacks and information leaks. The efficient design of the defense model is crucial for safeguarding smart city cyberspace, as present cyber security advancements are not keeping up with the rapid uptake of these technologies worldwide. The present study describes in detail the architecture of a smart city and the sophisticated types of flooding attacks that could target it. Also, the study examines the current literature on IoT security in terms of smart cities that will provide an outline for the concepts of cyber security, learning-based defense methodologies. In particular, several learning methods were quickly examined to overcome the impact of flooding attacks, including Instance Supervised Learning (ISL), Sequence Learning, which is also supervised. Ont the other hand, the other variant of learning that is semi-supervised also introduced, sucha as the Reinforcement Learning and the Hybrid Learning. Additionally, the review illustrates the recent datasets that have been used to evaluate the efficiency of flooding defense systems. © 2024, Penerbit UTHM. All rights reserved.
Author Keywords	Flooding attacks; IoT; learning detection methods; smart city

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