PhishGuard URL Phishing Detection System is an AI-powered web application designed to protect users from phishing websites. It uses machine learning to analyze URLs in real-time and classify them as safe or malicious.
Built as a full-stack project combining Flask (Python), scikit-learn for ML, and a modern glassmorphic UI. The system performs WHOIS lookups, DNS resolution, and extracts 12+ features from each URL to make accurate predictions.
PhishGuard is proudly developed as an MCA Final Year Project, representing a collaborative innovation in cybersecurity and machine learning. This system reflects the combined technical expertise, research effort, and full-stack development skills of:
This system uses state-of-the-art machine learning to analyze URLs in real-time, protecting you from sophisticated phishing attacks.
Our engine looks at over 12 distinct signals to determine risk.
Random Forest model detects subtle URL obfuscation techniques that bypass traditional filters.
Real-time domain age and registrar verification to spot newly registered malicious domains.
Follows URL shorteners and complex redirect chains to analyze the final destination.
Every scan is logged in your personal dashboard so you can review past checks anytime.
From paste to verdict in seconds. Here's what happens behind the scenes when you scan a URL.
Enter any suspicious link into the scanner. We auto-detect the protocol and www prefix.
We extract 12+ features: URL length, special chars, digit ratio, HTTPS status, subdomain count, and more.
A trained Random Forest model with 100 decision trees votes on whether the URL is phishing or safe.
Get a clear Safe/Phishing verdict with confidence score, domain WHOIS info, and safety recommendations.
URL Features Analyzed
Decision Trees Voting
Average Scan Time
Domain Intelligence