phising-detection

A real-time phising detection and URL scanning browser extension just by hovering (Now Available on Edge Addons)

Production-grade MLOps pipeline for phishing URL detection with modular training, MLflow experiment tracking, FastAPI inference, and automated CI/CD to AWS

ML-powered phishing URL detection using Flask, Random Forest, and 30 heuristic security features.

AI-powered phishing detection platform for real-time URL analysis and intelligent web threat detection.

🛡️ PhishGuard – Real-time phishing detection system. Hybrid blacklist/heuristic engine + cross‑browser extension. Built with Django & Python.

ML phishing URL detector using LightGBM — 93.2% accuracy

Real-time phishing URL detection using Random Forest ML — 99.90% AUC-ROC

Interpretable phishing email detector built with scikit-learn — TF-IDF + engineered structural features, 5-model comparison, threshold tuning, and feature importance analysis for SOC-ready explainability.

Performed email forensic analysis to detect spoofed emails through header analysis, authentication validation (SPF/DKIM/DMARC), and OSINT-based IP attribution

Production-ready phishing email detector with BERT Transformer + Bidirectional LSTM deep learning models. Features live UI with model agreement, confidence comparison, final verdict, risk indicator, and processing time.

Everyone has done this during their university days, and so have we. This is the repository for our SIH and Internal Hackathon Project (2023). There's not much here; the SOURCE CODE, PPT, and ARCHITECTURE are archived and private. Thank you for visiting.

end-to-end phishing detection system using ML, FastAPI, Docker, and AWS with CI/CD deployment

Explainable AI scam-message detector built with React, Flask, scikit-learn and SQLite.