Please use this identifier to cite or link to this item:
http://hdl.handle.net/2080/4628
Title: | A Deep Learning Framework for Malware Classification using NLP Techniques |
Authors: | Gond, Bishwajit Prasad Singh, Atul Kumar Mohapatra, Durga Prasad |
Keywords: | API call Deep Learning Malware Malware classifier n-grams Portable executable |
Issue Date: | Jun-2024 |
Citation: | 15th International IEEE Conference on Computing, Communication and Networking Technologies (ICCCNT), IIT Mandi, Himachal Pradesh, India, 24-28 June 2024 |
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. |
Description: | Copyright belongs to proceeding publisher |
URI: | http://hdl.handle.net/2080/4628 |
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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