Data Mining for Cybersecurity

This repository contains materials, scripts, and notebooks related to Data Mining techniques applied to Cybersecurity.
The project focuses on analyzing security-related datasets to extract insights, detect patterns, and support defensive or analytical tasks.

Project Goals

• Apply data mining and data analysis techniques to cybersecurity use cases
• Explore datasets related to security events, attacks, or network behavior
• Provide examples and experiments useful for academic or learning purposes

Repository Contents

Typical contents of the repository may include:

• Python scripts for data preprocessing and analysis
• Jupyter notebooks for exploratory data analysis and experiments
• Datasets (or references to external datasets)
• Documentation and notes related to cybersecurity data mining techniques

Requirements

• Python 3.x
• Common Python data science libraries:
  • pandas
  • numpy
  • matplotlib / seaborn
  • scikit-learn
  • jupyter

Setup

• Create and activate a virtual environment (recommended):

python3 -m venv .venv
source .venv/bin/activate
pip install pandas numpy matplotlib seaborn scikit-learn jupyter

Usage

Typical workflow:

• Load or import the cybersecurity dataset
• Perform data cleaning and preprocessing
• Apply data mining or machine learning techniques
• Analyze and visualize results
• Interpret findings in a cybersecurity context Scripts and notebooks are meant to be adapted and extended based on the specific dataset or experiment.

License

This project is released under the MIT License.