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http://dx.doi.org/10.14400/JDC.2021.19.9.463

A comparative study of the performance of machine learning algorithms to detect malicious traffic in IoT networks  

Hyun, Mi-Jin (Division of Mathmatics, Science, and Computers, Kyungnam University)
Publication Information
Journal of Digital Convergence / v.19, no.9, 2021 , pp. 463-468 More about this Journal
Abstract
Although the IoT is showing explosive growth due to the development of technology and the spread of IoT devices and activation of services, serious security risks and financial damage are occurring due to the activities of various botnets. Therefore, it is important to accurately and quickly detect the activities of these botnets. As security in the IoT environment has characteristics that require operation with minimum processing performance and memory, in this paper, the minimum characteristics for detection are selected, and KNN (K-Nearest Neighbor), Naïve Bayes, Decision Tree, Random A comparative study was conducted on the performance of machine learning algorithms such as Forest to detect botnet activity. Experimental results using the Bot-IoT dataset showed that KNN can detect DDoS, DoS, and Reconnaissance attacks most effectively and efficiently among the applied machine learning algorithms.
Keywords
IoT; Botnet; Machine Learning; Security; Data Sets;
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