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A Comparative Analysis of Base Learning and Ensemble Learning for Botnet Detection

Agboola, Sheriff (2019) A Comparative Analysis of Base Learning and Ensemble Learning for Botnet Detection. Masters thesis, Dublin, National College of Ireland.

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Abstract

The proliferation of botnets is one of the challenges faced in the realm of cyber security. Botnet are used in perpetuating malicious activities such as stealing personal data, password and sensible information belonging to organisations, click-fraud and performing Distributed Denial of Service (DDoS) attacks, sending of unsolicited emails etc. In this study, we compare the base learning model and ensemble machine learning model using the brute force search technique. Our approach focuses on four of the most commonly used machine learning methods namely Support Vector Machine (SVM), Decision Tree, Random Forest and Ada-Boost in detecting the Internet Relay Chat (IRC) botnet which is one of the oldest and most used type of botnet. The experimental results show that the detection accuracy of Random Forest is better than the rest of the machine learning methods, achieving an accuracy of 99.6% on a balanced dataset and a false positive of 0.2% on known IRC botnet dataset.

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
T Technology > T Technology (General) > Information Technology > Computer software

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: CAOIMHE NI MHAICIN
Date Deposited: 03 Apr 2020 13:32
Last Modified: 03 Apr 2020 13:32
URI: http://norma.ncirl.ie/id/eprint/4174

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