Phisherman: A Phishing Email Detection Browser Extension

Year : 2025 | Volume : 16 | Issue : 02 | Page : 99 105
    By

    Vaibhav Valmik Shermale,

  • Sarang Gopal More,

  • Darshan Mahesh Jadhav,

  • Om Devidas Chaudhari,

  1. Student, Department of Computer Engineering, Mumbai Educational Trust (MET) Institute of Engineering, Nashik, Maharashtra, India
  2. Student, Department of Computer Engineering, Mumbai Educational Trust (MET) Institute of Engineering, Nashik, Maharashtra, India
  3. Student, Department of Computer Engineering, Mumbai Educational Trust (MET) Institute of Engineering, Nashik, Maharashtra, India
  4. Student, Department of Computer Engineering, Mumbai Educational Trust (MET) Institute of Engineering, Nashik, Maharashtra, India

Abstract

Phishing attacks continue to pose significant security risks, exploiting email as a primary vector to deceive users and compromise sensitive information. To counter these threats, Phisherman presents a sophisticated, real-time phishing detection system that integrates both rule-based methods and deep learning for heightened accuracy. Built as a cross-browser extension, compatible with Chrome, Firefox, and Edge through the WebExtension API, Phisherman combines traditional verification checks, such as DNS blacklisting, SPF, DKIM, and DMARC, with an advanced 1D-CNN and Bi-GRU deep learning model. This hybrid approach allows Phisherman to identify a wide range of phishing tactics, from well-known techniques to emerging, more subtle patterns that evade conventional filters. Upon detection, the system automatically moves flagged emails to the spam folder and promptly alerts the user, thereby minimizing the risk of interaction with malicious content. By combining multiple verification layers with a user-friendly interface, Phisherman offers high detection accuracy, low false-positive rates, and seamless integration, establishing itself as a robust, accessible solution for enhanced email security in both individual and organizational contexts.

Keywords: Phishing, cyber security, browser extension, Bi-GRU, LSTM, 1D-CNNPD

[This article belongs to Journal of Computer Technology & Applications ]

How to cite this article:
Vaibhav Valmik Shermale, Sarang Gopal More, Darshan Mahesh Jadhav, Om Devidas Chaudhari. Phisherman: A Phishing Email Detection Browser Extension. Journal of Computer Technology & Applications. 2025; 16(02):99-105.
How to cite this URL:
Vaibhav Valmik Shermale, Sarang Gopal More, Darshan Mahesh Jadhav, Om Devidas Chaudhari. Phisherman: A Phishing Email Detection Browser Extension. Journal of Computer Technology & Applications. 2025; 16(02):99-105. Available from: https://journals.stmjournals.com/jocta/article=2025/view=209448


References

  1. Chavan D, Yawalkar PM. Privacy and Owner autherisation framework to manage keys. Int J Adv Res Innov Ideas Educ. 2017; 3(4): 785–795.
  2. Dabhade V, Alvi AS. An Energy efficient approach for Secure data communication using Pairwise key encryption in WSN. Neuro Quantology. 2022 Nov; 20(15): 6311–6321.
  3. Atawneh S, Aljehani H. Phishing email detection model using deep learning. Electronics. 2023 Oct 15; 12(20): 4261.
  4. Altwaijry N, Al-Turaiki I, Alotaibi R, Alakeel F. Advancing phishing email detection: A comparative study of deep learning models. Sensors. 2024 Mar 24; 24(7): 2077.
  5. Rawal S, Rawal B, Shaheen A, Malik S. Phishing detection in e-mails using machine learning. Int J Appl Inf Syst. 2017 Oct; 12(7): 21–4.
  6. Thakur K, Ali ML, Obaidat MA, Kamruzzaman A. A systematic review on deep-learning-based phishing email detection. Electronics. 2023 Nov 5; 12(21): 4545.
  7. Chen H, Hossain M. Developing a Google Chrome Extension for Detecting Phishing Emails. EPiC Ser Comput. 2021; 77: 13–22.
  8. Sahingoz OK, Buber E, Demir O, Diri B. Machine learning based phishing detection from URLs. Expert Syst Appl. 2019 Mar 1; 117: 345–57.
  9. Bagui S, Nandi D, Bagui S, White RJ. Classifying phishing email using machine learning and deep learning. In 2019 IEEE International Conference on Cyber Security and Protection of Digital Services (Cyber Security). 2019 Jun 3; 1–2.
  10. Shalini L, Manvi SS, Gowda NC, Manasa KN. Detection of phishing emails using machine learning and deep learning. In 2022 IEEE 7th International conference on communication and electronics systems (ICCES). 2022 Jun 22; 1237–1243.
Regular Issue Subscription Review Article
Volume 16
Issue 02
Received 03/03/2025
Accepted 21/04/2025
Published 03/05/2025
Publication Time 61 Days
298

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