
International Journal on Science and Technology
E-ISSN: 2229-7677
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Impact Factor: 9.88
A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal
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Volume 16 Issue 2
2025
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A Phishing URL Detection Tool PhishGuard Using Python
Author(s) | Vasundhara S, Shri Varshini K, Janani P, Dr. S. Mohandoss |
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Country | India |
Abstract | PhishGuard is an API-based security tool that uses machine learning algorithms to identify and analyze phishing URLs, complemented by the VirusTotal API for full-fledged risk analysis. The tool maximizes its analytical power by integrating machine learning (ML) algorithms for automated URL extraction, analysis, and reporting, giving users useful details regarding the possible danger level of suspicious URLs. Some of the major features are automatic URL scanning by a mere keyboard shortcut (Shift + Click), live analysis of phishing likelihood optimized by ML-based predictions, and PDF report generation with detailed information. PhishGuard also provides visual insights in the form of pie charts, classifying URLs according to their risk levels. All scanned URLs are logged by the system and stored in categorized log files for future use. Developed with Python, PhishGuard combines technologies like Matplotlib for visualization, PyQt6 for the interface, and FPDF for report generation. PhishGuard is made to be easy to use, with simple installation and uninstallation procedures. PhishGuard seeks to improve browsing security by giving users a safe and efficient way of detecting and avoiding malicious URLs using the power of machine learning. |
Keywords | PhishGuard, Phishing Detection, URL Analysis, VirusTotal API, Machine Learning, Risk Assessment, Python, Cybersecurity, Visualization |
Field | Computer |
Published In | Volume 16, Issue 2, April-June 2025 |
Published On | 2025-04-21 |
Cite This | A Phishing URL Detection Tool PhishGuard Using Python - Vasundhara S, Shri Varshini K, Janani P, Dr. S. Mohandoss - IJSAT Volume 16, Issue 2, April-June 2025. DOI 10.71097/IJSAT.v16.i2.3887 |
DOI | https://doi.org/10.71097/IJSAT.v16.i2.3887 |
Short DOI | https://doi.org/g9gds7 |
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