IoT botnet detection in home IoT environment using machine learning

Fong, Zi Khang IoT botnet detection in home IoT environment using machine learning. Project Report. Melaka, Malaysia, Universiti Teknikal Malaysia Melaka. (Submitted)

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Abstract

With the rapid development of Internet of Things (IoT) technology, more and more devices are connected to each other through the Internet, forming a large and vulnerable network system. The widespread application and connectivity of these devices enhance the risk of Botnet attacks. This paper aims to develop an intelligent detection system to detect Botnet activities in the Internet of Things through artificial intelligence algorithms to improve detection efficiency and accuracy. Through this detection system, potential network attacks can be discovered and blocked in a timely manner, effectively protecting the security of IoT devices and data. At the same time, this will also promote the development of Botnet detection technology and improve the security and stability of the network. The research also includes the analysis and comparison of different features to optimize the feature selection process, and by evaluating and comparing multiple machine learning classifiers, determine the most effective classifier in the home IoT environment. These contributions not only improve the overall performance of the Botnet detection system, but also provide valuable data and empirical basis for future research.

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