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Improved Phishing Attack Detection with Machine Learning: A Comprehensive Evaluation of Classifiers and Features

Authors :
Sibel Kapan
Efnan Sora Gunal
Source :
Applied Sciences, Vol 13, Iss 24, p 13269 (2023)
Publication Year :
2023
Publisher :
MDPI AG, 2023.

Abstract

In phishing attack detection, machine learning-based approaches are more effective than simple blacklisting strategies, as they can adapt to new types of attacks and do not require manual updates. However, for these approaches, the choice of features and classifiers directly influences detection performance. Therefore, in this work, the contributions of various features and classifiers to detecting phishing attacks were thoroughly analyzed to find the best classifier and feature set in terms of different performance metrics including accuracy, precision, recall, F1-score, and classification time. For this purpose, a brand-new phishing dataset was prepared and made publicly available. Using an exhaustive strategy, every combination of the feature groups was fed into various classifiers to detect phishing websites. Two existing benchmark datasets were also used in addition to ours for further analysis. The experimental results revealed that the features based on the uniform resource locator (URL) and hypertext transfer protocol (HTTP), rather than all features, offered the best performance. Also, the decision tree classifier surpassed the others, achieving an F1-score of 0.99 and being one of the fastest classifiers overall.

Details

Language :
English
ISSN :
20763417
Volume :
13
Issue :
24
Database :
Directory of Open Access Journals
Journal :
Applied Sciences
Publication Type :
Academic Journal
Accession number :
edsdoj.140b475961ce4c15b793574c885ca376
Document Type :
article
Full Text :
https://doi.org/10.3390/app132413269