🛡️✨ Bot Detection Machine 🚀🕵️‍♀️

A browser-based bot detection simulation that analyzes user behavior to determine whether a request comes from a bot or a human. This project simulates various behavioral signals and combines them using advanced heuristics for accurate bot detection.

🚀 Features

• 🕵️ User Agent Analysis: Detects bot-related identifiers in the user agent string.
• 🖱️ Mouse Movement Analysis: Evaluates variability and natural randomness in mouse movements.
• ⌨️ Keystroke Timing Analysis: Detects suspiciously consistent typing patterns.
• 🕒 Click Interval Analysis: Reviews click timings to catch robotic precision.
• 📊 Advanced Heuristics: Combines multiple analyses for a more accurate detection score.
• 🔴 Live Simulation Mode: Simulate a continuous stream of requests in real time.

📂 Project Structure

├── 📄 index.html             # Main HTML file
├── 📂 src/
│   ├── ⚡ utils.js          # Utility functions for simulating behavior
│   ├── 🧠 detector.js       # BotDetector class with detection logic
│   └── 📘 app.js            # Main script handling simulation and display

🛠️ Getting Started

Follow these steps to set up and run the project locally.

1. 🛜 Clone the Repository:

git clone https://github.com/MahmudGalib7/Bot-Detection-Machine.git
cd Bot-Detection-Machine

2. 🌐 Launch the Project:

• 🟩 Option 1: Open index.html directly in your browser.
• 🟨 Option 2: Use Visual Studio Code with the Live Server extension for an enhanced development experience.

3. 🎯 Simulate Requests:

The page loads with sample requests and displays detection results. To enable live request simulation, uncomment the following line in app.js:

simulateLiveRequests(10);

Adjust the number of requests as needed!

📘 File Overview

• 🏗️ index.html: Main HTML structure, linking all scripts.
• 🛠️ src/utils.js: Functions to generate simulated behavior (mouse movements, keystrokes, clicks, IP addresses).
• 🔍 src/detector.js: BotDetector class implementing various detection methods.
• 🧩 src/app.js: Manages request creation, detection, and result display.

🤝 Contributing

Contributions are welcome! If you have ideas for improvements or spot bugs, feel free to open an issue or submit a pull request.

📝 License

This project is provided as-is for educational purposes. Feel free to modify and build upon it.

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If you’d like me to add installation requirements, usage examples, or more detailed explanations of the detection algorithms, just let me know! 🚀✨