MALSIGHT: Exploring Malicious Source Code and Benign Pseudocode for Iterative Binary Malware Summarization

Binary malware summarization aims to automatically generate human-readable descriptions of malware behaviors from executable files, facilitating tasks like malware cracking and detection. Previous methods based on Large Language Models (LLMs) have shown great promise. However, they still face signif...

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Veröffentlicht in:	arXiv.org 2024-11
Hauptverfasser:	Lu, Haolang, Peng, Hongrui, Guoshun Nan, Cui, Jiaoyang, Wang, Cheng, Jin, Weifei, Wang, Songtao, Pan, Shengli, Tao, Xiaofeng
Format:	Artikel
Sprache:	eng
Schlagworte:	Datasets Descriptions Large language models Malware Source code Summaries Usability
Online-Zugang:	Volltext
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