CrackShark is an AI-driven cybersecurity tool designed to detect potential botnet activity and alert sysadmins to enact change. Additionally, CrackSharp is able to save IP addresses to a database of known botnets which can be used to block incoming traffic temporarily or permanently. The AI model behind this uses a Random Forest classification algorithm in order to determine if a subset of internet traffic packet data follows similar patterns to the labeled training data. Overall CrackSharp offers a complete solution for network security professionals or organizations looking to protect their infrastructure from DDOS or DOS attacks popular in the modern age.

Our mission is to provide a lightweight, open-source, and user-friendly solution for detecting botnet activity in real-time, helping organizations and individuals secure their networks against persistent cyber threats.

• Real-time automated network monitoring
• Suspicious IP Tracking
• Process and save analyzed network traffic data in house
• Queue based log analysis
• GUI based concurrent visualization

1. Clone Repository:

   git clone git@github.com:ItsKieren/CTC_MLProject.git
   cd CTC_MLProject

2. Set Up Environment:

   python3 -m venv venv
   source venv/bin/activate  # Linux/macOS
   .\venv\Scripts\activate   # Windows

3. Install Dependencies:

   # Ensure Python 3.10.12 is installed
   pip install -r requirements.txt

4. Unzip Model:

   7z x CTC_MLProeject/models/random_forest_model.7z

5. Run Application:

   python dump.py
   python monitor.py

We welcome contributions! Please follow these steps:

1. Fork the repository
2. Create a new branch for your feature or bugfix
3. Submit a pull request with a detailed description of your changes

This project uses the following technologies, libraries, and datasets:

Languages - Python, Bash
Frameworks - Flask, Bootstrap
Libraries - Scapy, Scikit-learn, Pandas, NumPy, Chart.js
Training Dataset - DReLAB (Deep REinforcement Learning Adversarial Botnet dataset)

MIT