PhishGuard: ML-Based Phishing URL Detector with Virus Total API and Chrome Extension

Phishing is one of the most prevalent cyber threats that targets users by deceiving them into visiting fraud websites that mimic legitimate ones. These websites often steal sensitive information such as login credentials, financial data, or personal details. With the rapid growth of online platforms and e-commerce, phishing attacks have become more advanced, making it crucial to develop automated detection systems that can identify and block such malicious URLs in real time. The proposed project, PhishGuard, aims to design and implement a machine learning–based phishing URL detection system that can accurately classify URLs as legitimate or phishing. The system extracts multiple features from the URL, including lexical characteristics, domain-based attributes, and external metadata such as SSL certification and Google indexing status. Using these features, a trained classification model predicts the likelihood of the URL being fraud. The solution is implemented with a Flask-based web interface, allowing users to input any URL and instantly receive a phishing risk analysis report. The system leverages a trained dataset of phishing and legitimate URLs to achieve high accuracy and reliability. Additionally, it also provides the result of VirusTotal API which is a famous for detecting the maliciousness of website, files, emails, etc. PhishGuard contributes to cybersecurity awareness and prevention by helping users detect phishing threats proactively before interacting with harmful websites. This approach is lightweight, scalable, and suitable for integration into browsers, email filters, or enterprise-level security tools.