Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/4576
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dc.contributor.authorGond, Bishwajit Prasad-
dc.contributor.authorRajneekant, .-
dc.contributor.authorKishor, Pushkar-
dc.contributor.authorMohapatra, Durga Prasad-
dc.date.accessioned2024-05-23T11:14:07Z-
dc.date.available2024-05-23T11:14:07Z-
dc.date.issued2024-05-
dc.identifier.citation4th International Conference on Machine Learning and Big Data Analytics (ICMLBDA), NIT Kurukshetra, India, Hybrid Mode, 09-11 May 2024en_US
dc.identifier.urihttp://hdl.handle.net/2080/4576-
dc.descriptionCopyright belongs to proceeding publisheren_US
dc.description.abstractThis 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.en_US
dc.subjectAPI callsen_US
dc.subjectMalware Classifieren_US
dc.subject𝑛-gramsen_US
dc.subjectPortable executableen_US
dc.titleMalware Classification Leveraging NLP & Machine Learning for Enhanced Accuracyen_US
dc.typeArticleen_US
Appears in Collections:Conference Papers

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