Malware Classification Leveraging NLP & Machine Learning for Enhanced Accuracy
Abstract
This paper investigates the application of natural language processing (NLP)-based n-gram analysis and machine learning techniques to enhance malware classification. We explore how NLP can be used to extract and analyze textual features from malware samples through n-grams, contiguous string or API call sequences. This approach effectively captures distinctive linguistic patterns among malware and benign families, enabling finer-grained classification. We delve into n-gram size selection, feature representation, and classification algorithms. While evaluating our proposed method on real-world malware samples, we observe significantly improved accuracy compared to the traditional methods. By implementing our n-gram approach, we achieved an accuracy of 99.02% across various machine learning algorithms by using hybrid feature selection technique to address high dimensionality. Hybrid feature selection technique reduces the feature set to only 1.6% of the original features.
Cite
@article{arxiv.2506.16224,
title = {Malware Classification Leveraging NLP & Machine Learning for Enhanced Accuracy},
author = {Bishwajit Prasad Gond and Rajneekant and Pushkar Kishore and Durga Prasad Mohapatra},
journal= {arXiv preprint arXiv:2506.16224},
year = {2026}
}
Comments
After review, I found errors in methodology and results that invalidate the conclusions. Discovered via peer feedback and self-verification, these issues necessitate withdrawal to maintain scientific integrity