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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

PhishRepo: A Seamless Collection of Phishing Data to Fill a Research Gap in the Phishing Domain

Author 1: Subhash Ariyadasa
Author 2: Shantha Fernando
Author 3: Subha Fernando

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2022.0130597

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 5, 2022.

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Abstract: Machine learning-based anti-phishing solutions face various challenges in collecting diverse multi-modal phishing data. As a result, most previous works have trained with little or no multi-modal data, which opens several drawbacks. Therefore, this study aims to develop a phishing data repository to meet the diverse data needs of the anti-phishing domain. As a result, a gap-filling solution named PhishRepo was proposed as an online data repository that collects, verifies, disseminates, and archives phishing data. It includes innovative design aspects such as automated submission, deduplication filtering, automated verification, crowdsourcing-based human interaction, an objection reporting window, and target attack prevention techniques. Moreover, the deduplication filter, used for the first time in phishing data collection, significantly impacted the collection process. It eliminated the duplicate data, which causes one of the most common machine learning errors known as data leakage. In addition, PhishRepo enables researchers to apply modern machine learning techniques effectively and supports them by eliminating phishing data hassle. Therefore, more thoughtful use of PhishRepo will lead to effective anti-phishing solutions in the future, minimising the social engineering crime called phishing.

Keywords: Cyberattack; crowdsourcing; internet security; phishing; machine learning; multi-modal data

Subhash Ariyadasa, Shantha Fernando and Subha Fernando, “PhishRepo: A Seamless Collection of Phishing Data to Fill a Research Gap in the Phishing Domain” International Journal of Advanced Computer Science and Applications(IJACSA), 13(5), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130597

@article{Ariyadasa2022,
title = {PhishRepo: A Seamless Collection of Phishing Data to Fill a Research Gap in the Phishing Domain},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130597},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130597},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {5},
author = {Subhash Ariyadasa and Shantha Fernando and Subha Fernando}
}


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