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


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


  1. Clark JW. “Trends in social engineering: Securing the weakest link”.
  2. Kumaran N and Lugani S. “Identity and security. Protecting businesses against cyber threats during COVID-19 and beyond”.
  3. Dewis Molly and Thiago Viana. "Phish Responder: A Hybrid Machine Learning Approach to Detect Phishing and Spam Emails”. Applied System Innovation73 (2022): 2-19.
  4. Afandi Nurul A and Isredza R A Hamid. "Covid-19 Phishing Detection Based on Hyperlink Using KNearest Neighbor (KNN) Algorithm”. Applied Information Technology and Computer Science2 (2021): 387-301.
  5. Akdemir Naci and Serkan Yenal. "How Phishers Exploit the Coronavirus Pandemic: A Content Analysis of COVID-19 Themed Phishing Emails”. (2021).
  6. Ispahany Jamil and Rafiqul Islam. "Detecting Malicious Urls of COVID-19 Pandemic Using ML Techniques”. 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and Other Affiliated Events (PerCom Workshops), (2021).
  7. Al-Qahtani Ali F and Stefano Cresci. “The COVID-19 Scamdemic: A Survey of Phishing Attacks and Their Countermeasures during COVID-19”. IET Information Security (2022).
  8. Saha Ishita., et al. "Phishing Attacks Detection Using Deep Learning Approach”. Proceedings of the Third International Conference on Smart Systems and Inventive Technology (2020).
  9. Egozi Gal and Rakesh Verma. "Phishing Email Detection Using Robust NLP Techniques”. 2018 IEEE International Conference on Data Mining Workshops (ICDMW) (2018).
  10. Abdelhamid Neda., et al. "Phishing Detection: A Recent Intelligent Machine Learning Comparison Based on Models Content and Features”. 2017 IEEE International Conference on Intelligence and Security Informatics (ISI), (2017).
  11. Vrbanciˇcˇ Grega., et al. “Datasets for Phishing Websites Detection”. Elsevier (2020).
  12. Kawaoka Ryo., et al. "A First Look at COVID-19 Domain Names: Origin and Implications”. In Proceedings of the Passive and Active Measurement Conference 2021 (PAM 2021), (2021).
  13. Aljofey Ali., et al. "An Effective Detection Approach for Phishing Websites Using URL and HTML Features”. Scientific Reports (2022).


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: © 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.


Acceptance rate35%
Acceptance to publication20-30 days

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