Malware Classification Leveraging NLP & Machine Learning for Enhanced Accuracy

Bishwajit Prasad Gond, Rajneekant, Pushkar Kishore, Durga Prasad Mohapatra
6/19/2025
cs.CRcs.LG

No official code found. Best implementation: bishwajitprasadgond/MalwareClassification (2 stars, Apache-2.0, last commit 2024-06-06).

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.

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Code Implementations(1)

bishwajitprasadgond/MalwareClassificationCommunity100%
Apache-2.0

Malware Classification Leveraging NLP & Machine Learning Techniques for Enhanced Accuracy

20PythonMar 21, 20242 years agoApache-2.0

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Cite this paper

@article{gond2025malware,
  title  = {Malware Classification Leveraging NLP & Machine Learning for Enhanced Accuracy},
  author = {Bishwajit Prasad Gond and Rajneekant and Pushkar Kishore and Durga Prasad Mohapatra},
  year   = {2025},
  eprint = {2506.16224},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2506.16224v2}
}

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