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http://hdl.handle.net/2080/4628
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DC Field | Value | Language |
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dc.contributor.author | Gond, Bishwajit Prasad | - |
dc.contributor.author | Singh, Atul Kumar | - |
dc.contributor.author | Mohapatra, Durga Prasad | - |
dc.date.accessioned | 2024-08-01T13:46:06Z | - |
dc.date.available | 2024-08-01T13:46:06Z | - |
dc.date.issued | 2024-06 | - |
dc.identifier.citation | 15th International IEEE Conference on Computing, Communication and Networking Technologies (ICCCNT), IIT Mandi, Himachal Pradesh, India, 24-28 June 2024 | en_US |
dc.identifier.uri | http://hdl.handle.net/2080/4628 | - |
dc.description | Copyright belongs to proceeding publisher | en_US |
dc.description.abstract | Malware classification is a crucial aspect of cybersecurity, vital for recognizing and addressing potential threats. This study introduces a fresh perspective on malware classification utilizing Natural Language Processing (NLP) methods in conjunction with Deep Learning. We propose the use of n-grams of API call sequences, including both API names and their arguments, as a means to characterize malware behavior. By employing Deep Learning techniques, we can effectively capture the nuanced patterns and distinctions in malware behaviors, enabling a more comprehensive understanding of malware characteristics and facilitating accurate identification and classification of diverse malware strains. The key contributions of this study include leveraging n-grams of API call sequences as a novel feature representation, developing a Deep Learning-based classification model, and evaluating the proposed approach on a large-scale malware dataset, demonstrating significant improvements in classification accuracy and robustness compared to traditional methods. The findings of this research present a promising avenue for improving malware classification, ultimately strengthening the overall cybersecurity landscape by combining the strengths of NLP and Deep Learning to combat evolving cyber threats. | en_US |
dc.subject | API call | en_US |
dc.subject | Deep Learning | en_US |
dc.subject | Malware | en_US |
dc.subject | Malware classifier | en_US |
dc.subject | n-grams | en_US |
dc.subject | Portable executable | en_US |
dc.title | A Deep Learning Framework for Malware Classification using NLP Techniques | 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_ICCCNT_BPGond_A Deep.pdf | 859.63 kB | Adobe PDF | View/Open Request a copy |
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