Random forest explorations for URL classification

Weedon, Martyn, Tsaptsinos, Dimitris and Denholm-Price, James (2017) Random forest explorations for URL classification. In: ., ., (ed.) 2017 International Conference On Cyber Situational Awareness, Data Analytics And Assessment (Cyber SA). Institute of Electrical and Electronics Engineers, Inc. ISBN 9781509050604

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Abstract

Phishing is a major concern on the Internet today and many users are falling victim because of criminal’s deceitful tactics. Blacklisting is still the most common defence users have against such phishing websites, but is failing to cope with the increasing number. In recent years, researchers have devised modern ways of detecting such websites using machine learning. One such method is to create machine learnt models of URL features to classify whether URLs are phishing. However, there are varying opinions on what the best approach is for features and algorithms. In this paper, the objective is to evaluate the performance of the Random Forest algorithm using a lexical only dataset. The performance is benchmarked against other machine learning algorithms and additionally against those reported in the literature. Initial results from experiments indicate that the Random Forest algorithm performs the best yielding an 86.9% accuracy.

Item Type: Book Section
Additional Information: Published version of Weedon, Martyn, Tsaptsinos, Dimitris and Denholm-Price, James (2017) Random forest explorations for URL classification. In: 2017 International Conference On Cyber Situational Awareness, Data Analytics And Assessment (Cyber SA); 19-20 2017, London, UK
Uncontrolled Keywords: phishing; URL; machine learning; Random Forest; lexical features
Research Area: Computer science and informatics
Faculty, School or Research Centre: Faculty of Science, Engineering and Computing (until 2017) > School of Computing and Information Systems
Faculty of Science, Engineering and Computing (until 2017) > School of Mathematics
Depositing User: James Denholm-Price
Date Deposited: 25 Sep 2018 15:06
Last Modified: 26 Sep 2018 07:25
DOI: https://doi.org/10.1109/CyberSA.2017.8073403
URI: http://eprints.kingston.ac.uk/id/eprint/41980

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