This repository encompasses the implementation of five distinct machine learning algorithms designed for the task of malware classification. Leveraging Natural Language Processing (NLP) techniques such as tokenization and vectorization, alongside feature selection methods like Chi-square, this work aims to advance the field of malware analysis.

For a detailed understanding of the methodology and findings, please refer to the preprint version of the paper, available in PDF format:

The Impact of Feature Selection on Malware Classification Using Chi-Square and Machine Learning (PDF)

If you find this work useful for your research, please consider citing the paper:

@inproceedings{rasheed2023impact,
  title={The Impact of Feature Selection on Malware Classification Using Chi-Square and Machine Learning},
  author={Rasheed, Areeg Fahad and Zarkoosh, M and Al-Azzawi, Sana Sabah},
  booktitle={2023 9th International Conference on Computer and Communication Engineering (ICCCE)},
  pages={211--216},
  year={2023},
  organization={IEEE}
}