Phishing Link Prevention System

Abstract

Phishing attacks are a major cybersecurity threat that deceive users into revealing sensitive information such as login credentials, banking details, or personal data. The Phishing Link Prevention System is designed to detect and block malicious URLs before users can access them. This system analyzes URL features like domain structure, URL length, special characters, and HTTPS usage, combined with real-time blacklist databases and machine learning classification. Implemented as a browser extension, it offers real-time alerts and ensures a secure browsing experience by preventing access to phishing websites. This proactive tool enhances user safety by reducing the risk of identity theft, financial loss, and data breaches

Introduction

Phishing is a widespread cyberattack that tricks users into revealing sensitive information via fake websites or malicious links. To combat this, the Phishing Link Prevention System detects and blocks suspicious URLs before users interact with them. It combines URL analysis, blacklist checks, and machine learning for real-time protection, often implemented as a browser extension.

Literature Review

Several anti-phishing methods exist:

• Blacklist-based (e.g., Google Safe Browsing): Effective but fails on new/zero-day links.

• Heuristic-based: Examines URL features (length, IP presence, symbols) but may cause false positives.

• Machine learning (SVM, Random Forest, Deep Learning): Learns hidden patterns for higher accuracy.

• Recent research supports hybrid systems for better performance and reliability.

Proposed System

The system uses a multi-layered detection approach:

• Extracts key features from URLs (domain age, length, HTTPS usage, special characters).

• Applies a trained machine learning model (e.g., Random Forest or SVM) to classify URLs as safe or phishing.

• Verifies against an updated blacklist of known phishing domains.

• Integrated as a browser extension to provide real-time warnings and enhance user safety.

Methodology

1. Data Collection: Gathers phishing and legitimate URLs from sources like PhishTank and OpenPhish.

2. Feature Extraction: Analyzes URL attributes (e.g., number of subdomains, symbols, HTTPS, etc.).

3. Technologies Used:

   • Python, JavaScript

   • scikit-learn, Chrome Extension API

   • HTML/CSS, GitHub

System Design

• Modular Architecture:

  • Blacklist Module

  • Feature Extraction Module

  • ML Classification Module

  • User Alert Module

• URLs are checked in real-time. If flagged as phishing, the system immediately warns the user via the browser interface.

Results & Discussion

• The system achieved over 95% accuracy, with high precision and recall, using models like Random Forest.

• Real-time detection via the browser extension proved effective in preventing access to phishing websites before any harm could occur.

Conclusion

The Phishing Link Prevention System provides an effective and practical solution to combat phishing attacks by detecting and blocking malicious URLs in real time. By combining URL feature analysis, blacklist verification, and machine learning classification, the system enhances the accuracy and reliability of phishing detection. Implemented as a browser extension, it offers a user-friendly and efficient way to protect users from potential threats while browsing. The results demonstrate that this multi-layered approach can significantly reduce the risk of phishing attacks and improve overall online security. With further enhancements and regular updates, the system can be extended to offer even broader protection across different platforms and user environments.

References

[1] PhishTank. (2024). Phishing URL Data Feed. https://www.phishtank.com [2] OpenPhish. (2024). Real-time Phishing Intelligence. https://openphish.com [3] Jain, A. K., & Gupta, B. B. (2018). Phishing detection: Analysis of visual similarity-based approaches. Security and Privacy, 1(1), e9. [4] Abu-Nimeh, S., Nappa, D., Wang, X., & Nair, S. (2007). A comparison of machine learning techniques for phishing detection. In Proceedings of the anti-phishing working groups 2nd annual eCrime researchers summit (pp. 60–69). ACM. [5] Dhanalakshmi, R., & Sornalakshmi, S. (2021). Real-time URL phishing detection using machine learning. International Journal of Computer Applications, 183(15), 20–24. [6] scikit-learn Developers. (2024). scikit-learn: Machine Learning in Python. https://scikit-learn.org [7] Google Chrome Developers. (2024). Chrome Extensions Documentation https://developer.chrome.com/docs/extensions

Copyright

Copyright © 2025 Divyalakshmi J, Jasmine K, Murugesh Pandian P. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.