Acta Scientific COMPUTER SCIENCES

Research Article Volume 4 Issue 10

Phishing Detection for Covid-19 Theme-Based Email and Weblinks Using Machine Learning

Usman Ali* and Gul Bano

Department of Software Engineering, Mehran University of Engineering and Technology, Pakistan

*Corresponding Author: Usman Ali, Department of Software Engineering, Mehran University of Engineering and Technology, Pakistan

Received: May 09, 2023; Published: June 10, 2022

Abstract

During the COVID-19 pandemic, phishing frauds became more prevalent as the victim was easily deceived into clicking on the link that contained the latest information about COVID-19. Despite various ways proposed to overcome this problem, phishing attacks continue to increase. The focus of this study was Phishing Detection for Covid-19 Theme-Based Email and Weblinks using Machine Learning. The study was comprised of two parts. Web Links and Email Themed. Two types of datasets were selected for experiments. Dataset 1 contains Web URL data and was downloaded from Kaggle. Dataset 2 contains Email images and was downloaded from Google, and Bing search Engines. Different features were selected for the detection of Phishing. Python libraries and coding was used for the analysis. The voting technique of the Ensemble model was used. It was revealed during the study that Dataset 2 achieves the highest accuracy while Dataset 1 performs better for other performance measures. Interesting concepts were found during the study

Keywords: Phishing; Email; URL; HTTP; DNS; ML

References

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Citation

Citation: Usman Ali and Gul Bano., et al. “Phishing Detection for Covid-19 Theme-Based Email and Weblinks Using Machine Learning".Acta Scientific Computer Sciences 5.7 (2022): 03-08.

Copyright

Copyright: © 2022 Usman Ali and Gul Bano., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.




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Acceptance rate35%
Acceptance to publication20-30 days

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