Network-Based Detection of Mirai Botnet Using Machine Learning and Feature Selection Methods

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Author(s): Ahmad Al-Qerem (Zarqa University, Jordan), Bushra Mohammed Abutahoun (Princess Sumaya University for Technology, Jordan), Shadi Ismail Nashwan (Computer Science Department, Jouf University, Saudi Arabia), Shatha Shakhatreh (Princess Sumaya University for Technology, Jordan), Mohammad Alauthman (Zarqa University, Jordan)and Ammar Almomani (Department of Information Technology, Al-Huson University College, Al-Balqa Applied University, Jordan)
Copyright: 2020
Pages: 11
Source title: Handbook of Research on Multimedia Cyber Security
Source Author(s)/Editor(s): Brij B. Gupta (National Institute of Technology, Kurukshetra, India)and Deepak Gupta (LoginRadius Inc., Canada)
DOI: 10.4018/978-1-7998-2701-6.ch016

Keywords: Cyber and Network Security / Information Science Reference / Media & Communications / Multimedia Technology

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Abstract

The spread of IoT devices is significantly increasing worldwide with a low design security that makes it more easily compromised than desktop computers. This gives rise to the phenomenon of IoT-based botnet attacks such as Mirai botnet, which have recently emerged as a high-profile threat that continues. Accurate and timely detection methods are required to identify these attacks and mitigate these new threats. To do so, this chapter will implement a network-based anomaly detection approach for the Mirai botnet using various machine learning and feature selection algorithms. Authors use Multiphase Genetic Algorithm section methods and PSO to select the best subfield of features capable of producing good overall classification results, and with this Feature Selection Algorithm, Random forest algorithm can detect all anomaly behavior with 100% accuracy.

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Network-Based Detection of Mirai Botnet Using Machine Learning and Feature Selection Methods

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