CyberRangers – Intelligent Network Threat Detection & Mitigation

A smart, AI-powered platform for identifying and responding to abnormal network traffic patterns in real time through advanced machine learning and interactive visualizations.

🔍 Project Summary

CyberRangers merges cutting-edge ML algorithms with live network data inspection to proactively detect and mitigate suspicious activity. The architecture includes:

• Backend: A Python-driven analytics engine using trained machine learning models
• Frontend: A responsive dashboard for visualization and simulated traffic interaction

🚀 Getting Started

🔧 Prerequisites

Backend

• Python 3.8 or higher
• Required Python packages (requirements.bat or requirements.txt)
• Minimum 8GB RAM recommended for training tasks

Frontend

• Node.js v14+
• npm or yarn package manager

🛠️ Installation

Backend Setup

1. Navigate to the backend folder:

cd backend

2. Install the necessary Python packages:

./requirements.bat
# or
pip install -r requirements.txt

3. (Optional) Prepare the dataset:

python data_preprocessing.py

Frontend Setup

1. Navigate to the frontend simulation directory:

cd frontendsim

2. Install dependencies:

npm install

💡 Usage

Backend Execution

1. Run the core backend application:

cd backend
python app.py

2. In a new terminal, start the data visualization panel:

python dashboard_v3.py

The interface will be accessible at http://localhost:5000

Frontend Execution

1. To launch the frontend server:

cd frontendsim
npm run dev

The dashboard will be available at http://localhost:3000

For production deployment:

npm run build

🧠 System Features

• Live monitoring of network traffic
• Anomaly detection using autoencoder networks
• Graph-based analysis with GNNs
• Reinforcement learning for threat scoring
• Transparent AI-based decision-making
• Network simulation capabilities
• Interactive and analytical frontend dashboard

📁 Project Layout

Backend

• app.py – Primary backend entry point
• autoencoder_model.py – Handles anomaly detection
• gnn_model.py – Performs graph neural network analysis
• rl_threat_scorer.py – Scores threats using reinforcement learning
• xai_explainer.py – Provides explainability for AI decisions
• dashboard_v3.py – Launches visual monitoring interface

Frontend

• Built with ReactJS
• Visual components powered by D3.js
• Simulated traffic environment
• Real-time monitoring panels