Botnet Detection in IoT Devices using Gradient and Ada Boosting Algorithm

Veeranam Shanmugam, Sririshi (2022) Botnet Detection in IoT Devices using Gradient and Ada Boosting Algorithm. Masters thesis, Dublin, National College of Ireland.

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Abstract

Most people think of "Internet of Things" (IoT) gadgets as unusual computers that can send and receive data across a network through wireless connections. As a result of the convenience they provide, IoT devices have become commonplace in people's daily routines. Considering that the vast majority of IoT gadgets aren't computers, they lack basic security features. It's because of this that hackers target people's IoT gadgets in an effort to get their hands on their passwords and financial information. In this study, we use the public IoT botnet dataset to investigate the prevalence of botnets in IoT devices and propose a machine learning approach for detecting them quickly and accurately. In this study, we employed the Gradient boosting technique to efficiently process a massive dataset while maintaining high standards of precision, throughput, and detection. Also, the Ada boosting algorithm has been integrated for a higher prediction speed in the botnets.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Pantridge, Michael UNSPECIFIED
Uncontrolled Keywords:	IoT (Internet of Things) devices; Attack; Botnet; detection; Boosting algorithm; Gradient Boost; Ada Boost
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 T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Telecommunications > Computer networks > Internet of things Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Cyber Security
Depositing User:	Tamara Malone
Date Deposited:	05 May 2023 15:38
Last Modified:	05 May 2023 15:38
URI:	https://norma.ncirl.ie/id/eprint/6552

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