PhishBlocker: AI-Powered Phishing Detection System

A comprehensive phishing detection system combining ensemble machine learning models with Large Language Model (LLM) integration for real-time threat analysis. The system includes a browser extension, a forensic analytics dashboard, and a high-throughput RESTful API.

Intelligence Architecture

PhishBlocker utilizes a multi-layered defense strategy:

• Ensemble ML Engine: Leverages LightGBM, TensorFlow, and Transformer-based URL analysis.
• LLM Verification: Deep contextual threat assessment via Google Gemini.
• Distributed Telemetry: Real-time stats synchronization across all neural endpoints.

Key Features

Multi-Layer Detection

• Vector Analysis: Lexical and structural URL inspection.
• Contextual Insight: LLM-driven forensic reporting for complex threats.
• Performance: Sub-100ms response times for core ML predictions.
• Accuracy: 98.3% verified detection rate in benchmark testing.

Protection Suite

• Browser Extension: Real-time page scanning during navigation.
• Forensic Dashboard: Live threat monitoring and URL probe gateway.
• Whitelisting: Trusted enclave management for verified domains.
• System Bypass: Robust administrative controls for false-positive override.

System Architecture

Detailed technical specifications can be found in ARCHITECTURE.md.

[ UI Layer ] <--- [ API Gateway ] <--- [ Intelligence Engine ] <--- [ Data Layer ]
  Extension         FastAPI             ML Models (LGBM/TF)       PostgreSQL
  Dashboard         Redis Cache         Gemini LLM Integration    Redis Persistence

Quick Start

Prerequisites

• Docker and Docker Compose
• Node.js 20+ (Development)
• Python 3.11+ (Development)
• Google Gemini API Key

Installation

1. Clone Repository

   git clone https://github.com/roshankumar0036singh/PhishBlocker.git
   cd PhishBlocker

2. Neural Model Setup The models are excluded from Git to keep the repository lightweight.

   • Download the model bundle from GitHub Releases.
   • Place all files (.h5, .txt, .pkl, .json) into the models/ directory.
   • For the extension, ensure url_classifier.onnx is in extension-react/public/models/.

3. Environment Configuration Copy .env.production to .env and provide your API credentials.

   cp .env.production .env

4. Deployment

   docker-compose up -d

Access Points

• Dashboard: http://localhost:3000
• API Service: http://localhost:8000
• API Documentation: http://localhost:8000/docs

Technical Documentation

• Architecture Overview
• Contributing Guidelines
• Security Protocols

License

PhishBlocker is released under the MIT License.

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Developed for Advanced Threat Detection and User Protection.