https://github.com/ekletony/cybervuln-ml
Machine Learning-based Categorization of Cybersecurity Vulnerabilities - IEEE UEMCON 2024
https://github.com/ekletony/cybervuln-ml
cves cybersecurity machine-learning nlp vulnerability-detection
Last synced: 8 months ago
JSON representation
Machine Learning-based Categorization of Cybersecurity Vulnerabilities - IEEE UEMCON 2024
- Host: GitHub
- URL: https://github.com/ekletony/cybervuln-ml
- Owner: EkleTony
- Created: 2023-07-25T18:19:24.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2025-03-13T17:52:24.000Z (over 1 year ago)
- Last Synced: 2025-03-13T18:39:13.233Z (over 1 year ago)
- Topics: cves, cybersecurity, machine-learning, nlp, vulnerability-detection
- Language: Jupyter Notebook
- Homepage: https://ieeexplore.ieee.org/abstract/document/10754709
- Size: 13 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Machine Learning-based: Enhanced Categorization of Cybersecurity Vulnerabilities
[](https://github.com/your-username/your-repo-name/blob/main/LICENSE)
[](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10754709)
[](https://scholar.google.com/citations?view_op=view_citation&hl=en&user=fk1n8VQAAAAJ&citation_for_view=fk1n8VQAAAAJ:qjMakFHDy7sC)
This repository contains the code, datasets, and results of our research on **Machine Learning-based categorization of cybersecurity vulnerabilities in software**, published at **2024 IEEE UEMCON**. The project was also awarded **Best Research Poster** at Tennessee Tech’s Annual Research Conference.
## Table of Contents
- [Overview](#overview)
- [Research Paper & Poster](#research-paper--poster)
- [Installation](#installation)
- [Usage](#usage)
- [License](#license)
- [Contact](#contact)
## Overview
Software vulnerabilities pose security risks, and traditional classification methods can be slow and error-prone. This project utilizes **Machine Learning** to automate and improve the categorization of cybersecurity vulnerabilities.
## Research Paper & Poster
- **Paper:** [IEEE UEMCON 2024](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10754709)
- **Poster:** [Tennessee Tech Research Conference](https://scholar.google.com/citations?view_op=view_citation&hl=en&user=fk1n8VQAAAAJ&citation_for_view=fk1n8VQAAAAJ:qjMakFHDy7sC)
## Installation
Follow these steps to set up the project:
```bash
# Clone the repository
git clone https://github.com/EkleTony/CyberVuln-ML.git
# Navigate to the project directory
cd ML-CVE-Categorization
# Install required dependencies
pip install -r requirements.txt
```
### Requirements
The following dependencies are required to run the project:
- Python 3.x
- Jupyter Notebook
- NumPy
- Pandas
- Scikit-learn
- TensorFlow
- Torch
- Matplotlib
- Seaborn
- NLTK
- TQDM
- WordNetLemmatizer (NLTK)
- IMBlearn (SMOTE)
- OS
- RE (Regular Expressions)
- String
Ensure the dataset is placed in the correct directory before running the notebooks.
## Usage
Run the Jupyter Notebook using:
```bash
jupyter notebook JupyterNote_CVE_Prediction_Code.ipynb
```
## License
This project is licensed under the **MIT License**. You are free to use, modify, and distribute the dataset and code with proper attribution. However, if using this dataset in academic or research work, please **cite the following paper**:
```
@inproceedings{ekle2024enhanced,
title={Enhanced Categorization of Cybersecurity Vulnerabilities},
author={Ekle, Ocheme Anthony and Ulybyshev, Denis},
booktitle={2024 IEEE 15th Annual Ubiquitous Computing, Electronics \& Mobile Communication Conference (UEMCON)},
pages={800--806},
year={2024},
organization={IEEE}
}
```