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IoT Botnet Detection using Machine Learning Techniques

Jagannath, Suhas (2020) IoT Botnet Detection using Machine Learning Techniques. Masters thesis, Dublin, National College of Ireland.

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Abstract

The widespread use of the internet of things (IoT) has led to serious security problems such as the denial of service (DoS) attack caused by a large group of compromised IoT devices. The IoT devices can easily be compromised as they do not have a good security posture. IoT devices have full internet access without any packet filtration in place, making them suitable to be a part of the zombie network. Although many researchers have proposed various IoT botnet detection techniques, many challenges remain unaddressed. In this paper, various machine learning techniques are proposed to effectively identify the presence of IoT botnet. The detection models predict the IoT botnet based on the network traffic information. The proposed model uses feature selection to achieve a faster detection rate with less false positive. The Random Forest classifier model outperformed the other machine learning models and deep learning model with an accuracy of 94.47% with a lesser detection time. Therefore, this model can be considered as an appropriate solution to effectively detect the IoT botnet.
Keywords: Internet of things (IoT), IoT botnet, IoT botnet detection, dimensionality reduction, feature selection, random forest, AdaBoost, KNN, dense neural network.

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science

Q Science > QA Mathematics > Computer software > Computer Security
T Technology > T Technology (General) > Information Technology > Computer software > Computer Security
Divisions: School of Computing > Master of Science in Cyber Security
Depositing User: Dan English
Date Deposited: 26 Jan 2021 16:50
Last Modified: 26 Jan 2021 16:50
URI: http://norma.ncirl.ie/id/eprint/4497

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