Somethingphishy: A Phishing Detection Browser Extension Using Logistic Regression

The increased amount of phishing attacks during the COVID-19 Pandemic and subsequent years has prompted worries in the cybersecurity sector. Despite technological improvements and the implementation of new regulations and rules in the Philippines, phishing attacks remain a widespread and serious danger. The purpose of this investigation is to create an automated phishing detection browser plugin for Firefox and Chromium browsers. The plugin will be capable of identifying phishing links by analyzing various URL features, including the frequency of each special character and the number of redirects. Apart from other functions like allow and block lists and an appeals page for website owners, the mentioned browser plugin would be able to predict phishing websites and prohibit them. Tested on 1802 links, the plugin displays accuracy of 83.24 %, precision of 77.04 %, recall of 85.77 %, F1 score of 81.17 %, and ROC-AUC of 92.55 %. Following a “Acceptable” comment from the software quality evaluation questionnaire based on the ISO/IEC 25010:2023 criterion, the program also responded favorably.

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Publication Details

Published
2025-05-24
DOI
https://doi.org/10.1109/iscaie64985.2025.11081133
Primary Topic
Spam and Phishing Detection
Type
article
Field-Weighted Citation Impact
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article

Somethingphishy: A Phishing Detection Browser Extension Using Logistic Regression

Mhike Jolo Y. Cron, D. Amoroso, Jerian R. Peren, Mark Simon S. Fabros
Spam and Phishing Detection
article

Somethingphishy: A Phishing Detection Browser Extension Using Logistic Regression

Mhike Jolo Y. Cron, D. Amoroso, Jerian R. Peren, Mark Simon S. Fabros
article en

Abstract

The increased amount of phishing attacks during the COVID-19 Pandemic and subsequent years has prompted worries in the cybersecurity sector. Despite technological improvements and the implementation of new regulations and rules in the Philippines, phishing attacks remain a widespread and serious danger. The purpose of this investigation is to create an automated phishing detection browser plugin for Firefox and Chromium browsers. The plugin will be capable of identifying phishing links by analyzing various URL features, including the frequency of each special character and the number of redirects. Apart from other functions like allow and block lists and an appeals page for website owners, the mentioned browser plugin would be able to predict phishing websites and prohibit them. Tested on 1802 links, the plugin displays accuracy of 83.24 %, precision of 77.04 %, recall of 85.77 %, F1 score of 81.17 %, and ROC-AUC of 92.55 %. Following a “Acceptable” comment from the software quality evaluation questionnaire based on the ISO/IEC 25010:2023 criterion, the program also responded favorably.

Lyceum of the Philippines University (PH)
Reduced inequalities
Openalex Percentile: Top 74%
Spam and Phishing Detection
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