Network threat classifier

About

This repository contains Python scripts for analyzing network traffic data to classify IPs as compromised or non-compromised using a Support Vector Machine (SVM) model and to predict network traffic flows using a Random Forest Regressor model.

This was a project for a Machine Learning class during my Master's degree. The presentation is written in Portuguese and can be accessed here.

Project Structure

network-threat-classifier/
├── src/                    # Python source code
│   ├── preprocessor.py     # Data preprocessing
│   ├── svn.py              # SVM classification
│   └── predictor.py        # Random Forest prediction
├── data/                   # Input data
│   └── data.csv
├── output/                 # Generated files (ignored by git)
│   ├── processed_data.csv
│   ├── roc_curve.png
│   └── prediction_comparison.png
├── docs/                   # Documentation
│   └── slides.pdf
├── run.py                  # Main runner script
├── Makefile                # Makefile for automation
├── requirements.txt        # Python dependencies
└── README.md

Run

Run the complete pipeline with a single command. The script will automatically create a virtual environment, install dependencies, and execute all steps.

Using Python script (works on all platforms):

python run.py

Using Makefile (Linux/Mac, or Windows with Make installed):

make

Both commands will automatically:

• Create a virtual environment if it doesn't exist
• Install all dependencies from requirements.txt
• Run the complete pipeline (preprocessing → classification → prediction)

Additional Makefile commands:

make preprocess  # Data preprocessing only
make classify    # Preprocessing + SVM classification
make predict     # Complete pipeline
make clean       # Remove generated files
make clean-venv  # Remove virtual environment
make help        # Show all available commands

Contributing

This repository is using Gitflow Workflow and Conventional Commits, so if you want to contribute:

• create a branch from develop branch;
• make your contributions;
• open a Pull Request to develop branch;
• wait for discussion and future approval;

I thank you in advance for any contribution.

Status

Finished

License

MIT