TEDVIL: Leveraging Transformer-Based Embeddings for Vulnerability Detection in Lifted Code

Ransomware and other malware inflict devastating financial and operational damage on organizations worldwide by exploiting deeply embedded, hard-to-detect vulnerabilities in their systems. Detecting these vulnerabilities in compiled code before malicious actors exploit them remains a critical challe...

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Bibliographic Details
Main Authors:	Gary A. McCully, John D. Hastings, Shengjie Xu
Format:	Article
Language:	English
Published:	IEEE 2025-01-01
Series:	IEEE Access
Subjects:	Bidirectional encoder binary security buffer overflow machine learning neural network transformer-based embedding
Online Access:	https://ieeexplore.ieee.org/document/10980253/
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TEDVIL: Leveraging Transformer-Based Embeddings for Vulnerability Detection in Lifted Code

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