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Botnet attack detection in IoT using machine learning models
Researcher with CAPE economic research and consulting US.
Review
International Journal of Science and Research Archive, 2024, 12(01), 2221–2229.
Article DOI: 10.30574/ijsra.2024.12.1.0936
Publication history:
Received on 16 April 2024; revised on 26 May 2024; accepted on 29 May 2024
Abstract:
Botnet is of great concern when dealing with security of computer networks globally. Bots readily attack network infrastructure through their malicious activities. It is pertinent to mitigate and control the level of threat posit by Bot and thus the need for advanced technologies in predicting their occurrences. Machine learning offered a greater support in this regard with ability to handle voluminous data from IoT devices and the robustness in predicting the potential attack from trained data. Both supervised (DT and RF) and unsupervised learning (Deep Learning) were used to investigate prediction of attack. Results of various machine learning models were compared along the performance metrics (Confusion Matrix, ROC Curves). The outcome showed that supervised learning did better with the study dataset than the unsupervised model.
Keywords:
Botnet; Machine Learning; Supervised; Unsupervised; Deep Learning
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0
