Building A Real Time Phishing Url Detector

Abstract

In Today’s Digital Era, Online Users Are Frequently Targeted By Deceptive Websites Designed To Steal Personal Credentials, Financial Data, And Sensitive Information. This Paper Presents Phishing Detector, An Intelligent Phishing Detection System That Leverages Machine Learning And Rule-based Techniques To Identify And Classify Malicious URLs In Real Time. The System Employs Random Forest And URL-based Feature Analysis To Evaluate Lexical And Structural Patterns, Achieving High Accuracy In Distinguishing Between Phishing And Legitimate Websites. Additionally, A Whitelist Verification Module Cross-checks Trusted Domains To Minimize False Positives And Enhance Detection Confidence. The Project Integrates A FastAPI Backend For Efficient Model Inference, A React.js Frontend For An Interactive And Responsive User Experience, And An SQLite Database For Logging Predictions And Domain Data. This Unified Architecture Forms A Scalable, Data-driven Solution For Detecting Evolving Phishing Threats, Providing Users With Secure Web Interaction And Reliable, Real-time Protection Against Online Scams..

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