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Title	Comparative Evaluation On Various Machine Learning Strategies Based On Identification Of Ddos Attacks In Iot Environment
ID_Doc	15037
Authors	Abinaya M.; Prabakeran S.; Kalpana M.
Year	2023
Published	2023 9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023
DOI	http://dx.doi.org/10.1109/ICACCS57279.2023.10112877
Abstract	IoT is a combination of networks that have the ability to collect and share the information through web. Though IoT is used in areas such as healthcare, smart cities, agriculture, industrial automation and home automation, it is also used in other fields. The challenges faced by the IoT devices are security, privacy, confidentiality, integrity, and technical complexity. In Distributed Denial of Service attacks, the attacker sends massive service request to the IoT device which cannot handle such high traffic, resulting in disruption of services to the end user. Modern types of DDOS attack are still more complex, and they are nearlyimpossible to identify or mitigate using classic intrusion detection systems and methods. In the last few years, Machine learning plays a significant role in identifying Distributed Denial of Service attacks effectively. We conducted a literature review on different types of DDoS attacks based on ML techniques. The model's performance is tested on several classifiers to choose the optimal classifier with high accuracy. We next go over each of the ML methods for IoT security in detail. The limitations and problems of current research are discussed. © 2023 IEEE.
Author Keywords	Distributed Denial of Service; Intrusion Detection System; IoT Attacks; IoT Security; Machine learning; NSL KDD

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