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SentinelFlow 🛡

License: MIT Python Version Electron Version GitHub Issues GitHub Stars FastAPI React TypeScript Scapy LangChain

Next-Generation Network Security with AI-Powered Threat Detection

🌟 Overview

SentinelFlow is an advanced network security monitoring and threat detection system that combines real-time packet analysis with AI-powered threat detection. Built with a modern tech stack including Python, Electron, and React, it provides a seamless and intuitive interface for monitoring network traffic and identifying potential security threats.

SentinelFlow Architecture

🎯 Why SentinelFlow?

  • Intelligent Threat Detection: Leverages multiple AI agents for comprehensive security analysis
  • Real-time Monitoring: Zero-latency packet analysis and instant threat alerts
  • User-Friendly Interface: Modern, intuitive UI for both beginners and security experts
  • Extensible Architecture: Easy to add new threat detection capabilities
  • Cross-Platform: Works seamlessly on Windows, macOS, and Linux

✨ Key Features

🔍 Network Analysis

  • Real-time Packet Capture: Powered by Scapy for high-performance packet interception
  • Protocol Support: TCP, UDP, HTTP/S, DNS, and more
  • Traffic Visualization: Real-time network traffic patterns and anomaly detection
  • Packet Filtering: Custom filters for targeted monitoring

🤖 AI-Powered Security

  • Multi-Agent System:
    • XSS Detection Agent
    • SQL Injection Analysis Agent
    • Payload Analysis Agent
    • Decision Making System
  • Machine Learning Models: Pattern recognition for zero-day threats
  • Natural Language Processing: Query your network logs in plain English

🛠 Security Profiles

  • Pre-configured Templates:
    • Backend Infrastructure Protection
    • Web Application Security
    • Database Access Monitoring
    • General Network Usage
  • Custom Profile Creation: Build your own security rules

📊 Analytics & Reporting

  • Real-time Dashboard: Live network statistics and threat alerts
  • Detailed Logs: Comprehensive activity tracking
  • Export Capabilities: Generate detailed security reports
  • Visualization: Interactive charts and graphs

🚀 Getting Started

System Requirements

  • OS: Windows 10+, macOS 10.15+, or Linux (Ubuntu 20.04+)
  • CPU: 2+ cores recommended
  • RAM: 4GB minimum, 8GB recommended
  • Storage: 1GB free space
  • Network: Active internet connection

Prerequisites

  • Python 3.11 or higher
  • Node.js 16+ and npm
  • Administrative privileges (for packet capture)

Quick Start

  1. Clone & Setup:
    # Clone repository
    git clone https://github.com/HackStyx/SentinelFlow.git
    cd SentinelFlow
    
    # Create virtual environment
    python -m venv venv
    source venv/bin/activate  # On Windows: .\venv\Scripts\activate
    
    # Install dependencies
    pip install -r requirements.txt
    cd frontend && npm install
    
    
  2. Configure Environment:
    # Copy example environment file
    cp .env.example .env
    # Edit .env with your settings
    nano .env  # or use VS Code: "code .env", Notepad: "notepad .env", etc.
    
    
    
  3. Launch Application:
    # Terminal 1: Start backend
    cd backend
    python server.py
    
    # Terminal 2: Start frontend
    cd frontend
    npm run dev
    
    

🏗 Architecture

SentinelFlow follows a microservices architecture with these key components:

  • Frontend: Electron + React application for UI
  • Backend: FastAPI server for API endpoints
  • AI Engine: LangChain-based multi-agent system
  • Packet Capture: Scapy-powered network monitoring
  • Database: Vector store for semantic log searching

🤝 Contributing

We welcome contributions! See our Contributing Guide for details.

  1. Fork the repository
  2. Create your feature branch
  3. Commit your changes
  4. Push to the branch
  5. Open a Pull Request

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.


About

Our project is an AI-powered network logger for real-time threat detection. It filters network activity using a multi-agent workflow, analyzes threats via a DAG structure, and enables natural language log queries with RAG for actionable insights.

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