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http://hdl.handle.net/2080/4576
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DC Field | Value | Language |
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dc.contributor.author | Gond, Bishwajit Prasad | - |
dc.contributor.author | Rajneekant, . | - |
dc.contributor.author | Kishor, Pushkar | - |
dc.contributor.author | Mohapatra, Durga Prasad | - |
dc.date.accessioned | 2024-05-23T11:14:07Z | - |
dc.date.available | 2024-05-23T11:14:07Z | - |
dc.date.issued | 2024-05 | - |
dc.identifier.citation | 4th International Conference on Machine Learning and Big Data Analytics (ICMLBDA), NIT Kurukshetra, India, Hybrid Mode, 09-11 May 2024 | en_US |
dc.identifier.uri | http://hdl.handle.net/2080/4576 | - |
dc.description | Copyright belongs to proceeding publisher | en_US |
dc.description.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. | en_US |
dc.subject | API calls | en_US |
dc.subject | Malware Classifier | en_US |
dc.subject | 𝑛-grams | en_US |
dc.subject | Portable executable | en_US |
dc.title | Malware Classification Leveraging NLP & Machine Learning for Enhanced Accuracy | en_US |
dc.type | Article | en_US |
Appears in Collections: | Conference Papers |
Files in This Item:
File | Description | Size | Format | |
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2024_ICMLABDA_BPGond_Malware_.pdf | 1.12 MB | Adobe PDF | View/Open Request a copy |
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