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[2506.16224] Malware Classification Leveraging NLP & Machine Learning for Enhanced Accuracy
Summary
This article explores the use of NLP and machine learning techniques for enhancing malware classification accuracy, achieving a notable 99.02% accuracy rate through innovative n-gram analysis.
Why It Matters
As cyber threats evolve, effective malware classification is crucial for cybersecurity. This research demonstrates how advanced NLP techniques can significantly improve detection rates, thereby enhancing security measures against malware attacks.
Key Takeaways
- NLP-based n-gram analysis improves malware classification accuracy.
- Achieved 99.02% accuracy using hybrid feature selection techniques.
- Focus on reducing feature dimensionality enhances model performance.
- Real-world malware samples were used for evaluation, ensuring relevance.
- The approach differentiates between benign and malicious software effectively.
Computer Science > Cryptography and Security arXiv:2506.16224 (cs) [Submitted on 19 Jun 2025 (v1), last revised 22 Feb 2026 (this version, v3)] Title:Malware Classification Leveraging NLP & Machine Learning for Enhanced Accuracy Authors:Bishwajit Prasad Gond, Rajneekant, Pushkar Kishore, Durga Prasad Mohapatra View a PDF of the paper titled Malware Classification Leveraging NLP & Machine Learning for Enhanced Accuracy, by Bishwajit Prasad Gond and 2 other authors View PDF HTML (experimental) 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. Commen...
