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Feature Selection for Machine Learning-based Phishing Websites Detection

Dangwal, Smriti and Moldovan, Arghir-Nicolae (2021) Feature Selection for Machine Learning-based Phishing Websites Detection. In: 2021 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA). IEEE, pp. 1-6. ISBN 978-1-6654-2529-2

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Phishing is a social engineering technique that is commonly used to deceive users in an attempt to obtain sensitive information such as username, passwords or credit card details. While there was extensive research on machine learning-based phishing detection, some prior works proposed a large number of features and not all of them are feasible to extract for real-time detection. This work combined two datasets with 30 and 48 features respectively, to identify 18 common features. Moreover, feature selection was conducted to identify 13 optimal features for a more robust model. A comparison with prior research works on the same datasets showed that the best models built on all features using the random forest algorithm scored lower on the 30 feature dataset, and achieved better performance on the 48 features dataset. The best model on the 13 features achieved an accuracy of 0.937.

Item Type: Book Section
Uncontrolled Keywords: Phishing detection; phishing websites; machine learning; feature selection
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
H Social Sciences > HV Social pathology. Social and public welfare > Criminology > Crimes and Offences > Cyber Crime
Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4150 Computer Network Resources > The Internet > World Wide Web > Websites
T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Telecommunications > The Internet > World Wide Web > Websites
Divisions: School of Computing > Staff Research and Publications
Depositing User: Clara Chan
Date Deposited: 08 Sep 2021 17:59
Last Modified: 07 Feb 2022 11:24

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