PHISHERMAN – A Phishing Emails Detecting Browser Extension

Notice

This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2025 | Volume : 16 | Issue : 02 | Page : –
    By

    Vaibhav Valmik Shermale,

  • Sarang Gopal More,

  • Darshan Mahesh Jadhav,

  • Om Devidas Chaudhari,

  1. Student, Dept. of Computer Engineering, MET Institute of Engineering Nashik, Maharashtra, India
  2. Student, Dept. of Computer Engineering, MET Institute of Engineering Nashik, Maharashtra, India
  3. Student, Dept. of Computer Engineering, MET Institute of Engineering Nashik, Maharashtra, India
  4. Student, Dept. of Computer Engineering, MET Institute of Engineering Nashik, Maharashtra, India

Abstract

document.addEventListener(‘DOMContentLoaded’,function(){frmFrontForm.scrollToID(‘frm_container_abs_187446’);});Edit Abstract & Keyword

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 verifica- tion 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, Phish- erman 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 Emails Detecting Browser Extension. Journal of Computer Technology & Applications. 2025; 16(02):-.
How to cite this URL:
Vaibhav Valmik Shermale, Sarang Gopal More, Darshan Mahesh Jadhav, Om Devidas Chaudhari. PHISHERMAN – A Phishing Emails Detecting Browser Extension. Journal of Computer Technology & Applications. 2025; 16(02):-. Available from: https://journals.stmjournals.com/jocta/article=2025/view=0



document.addEventListener(‘DOMContentLoaded’,function(){frmFrontForm.scrollToID(‘frm_container_ref_187446’);});Edit

References

  1. 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.
  2. Rawal S, Rawal B, Shaheen A, Malik S. Phishing detection in e-mails using machine learning. International Journal of Applied Information Systems. 2017 Oct;12(7):21-4.
  3. 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.
  4. Chen H, Hossain M. Developing a Google Chrome Extension for Detecting Phishing Emails. EPiC Series in Computing. 2021;77:13-22.
  5. Sahingoz OK, Buber E, Demir O, Diri B. Machine learning based phishing detection from URLs. Expert Systems with Applications. 2019 Mar 1;117:345-57.
  6. Bagui S, Nandi D, Bagui S, White RJ. Classifying phishing email using machine learning and deep learning. In2019 International Conference on Cyber Security and Protection of Digital Services (Cyber Security) 2019 Jun 3 (pp. 1-2). IEEE.
  7. Shalini L, Manvi SS, Gowda NC, Manasa KN. Detection of phishing emails using machine learning and deep learning. In2022 7th International conference on communication and electronics systems (ICCES) 2022 Jun 22 (pp. 1237-1243). IEEE.
  8. Chavan D, Yawalkar PM. PRIVACY AND OWNER AUTHERISATION FRAMEWORK TO MANAGE KEYS. IJARIIE; 2017, Vol-3 Issue-4, pp- 785-795.
  9. Dabhade V, Alvi AS. An Energy efficient approach for Secure data communication using Pairwise key encryptionin WSN., NeuroQuantology November2022 20(15), pp- 6311-6321.
  10. Atawneh S, Aljehani H. Phishing email detection model using deep learning. Electronics. 2023 Oct 15;12(20):4261.
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
145

[first_name] [last_name]

My IP

PlumX Metrics