phising-detection

AI-powered Cyber Asset Management, Vulnerability Assessment & Attack Surface Management Platform.

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

A cybersecurity lab project for simulating phishing attacks and analyzing detection techniques, with security awareness, email threat analysis, and defensive monitoring workflows.

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

Privacy-first Chrome Extension for phishing, scam, fake login, clipboard, and lookalike-domain detection with local trust scoring.

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

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

PhishGuard is a phishing‑focused training application that uses realistic, scenario‑based simulations to help users identify deceptive messages, spoofed senders and malicious URLs. It includes tailored tracks for students and elderly users, reflecting the specific phishing tactics commonly used against each group.

ML phishing URL detector using LightGBM — 93.2% accuracy

REST API deteksi phishing & URL berbahaya berbasis Python yang mengintegrasikan Google Safe Browsing, VirusTotal, dan URLScan.io.

Explainable threat intelligence for suspicious URLs and messages.

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

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.

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

AI-inspired phishing and scam risk detection tool built with Python and Streamlit.

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

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.

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.

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.