Malware Classification Leveraging NLP & Machine Learning for Enhanced Accuracy

2025-06-19 · Bishwajit Prasad Gond, Rajneekant, Pushkar Kishore, Durga Prasad Mohapatra

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.

📄 PDF Abstract BibTeX arXiv:2506.16224

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bishwajitprasadgond/malwareclassification 공식 구현 tf

Tasks

Classificationfeature selectionMalware Classification

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