Deep Learning-Based Detection of Code Execution Vulnerabilities in Binary Programs
He, Daojing Zhu, Shanshan Na, Qilin Cai, Yanchang Chan, Sammy Guizani, Mohsen
He, Daojing
Zhu, Shanshan
Na, Qilin
Cai, Yanchang
Chan, Sammy
Guizani, Mohsen
Supervisor
Department
Machine Learning
Embargo End Date
Type
Conference proceeding
Date
2025
License
Language
English
Collections
Research Projects
Organizational Units
Journal Issue
Abstract
Numerous incidents have shown that code execution vulnerabilities in binary files pose significant security risks, as attackers exploit them to compromise system integrity. This issue has garnered extensive attention from academia and industry. Binary code vulnerabilities impact both traditional and emerging software domains. Smart contracts, deployed in blockchain environments, face similar challenges. They must be compiled into machine-readable binary code before execution and are exposed to open networks, making them as frequent targets of attacks. Traditional vulnerability detection techniques rely on manual analysis or rule-based heuristics, which are time-consuming and inapplicable to detecting complex vulnerabilities. This study proposes a new approach using deep learning models to automatically detect code execution vulnerability paths in binary programs. Experiments demonstrate the method’s feasibility, achieving an accuracy rate of 97%–98% on the BERT+RNN model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Citation
D. He, S. Zhu, Q. Na, Y. Cai, S. Chan, and M. Guizani, “Deep Learning-Based Detection of Code Execution Vulnerabilities in Binary Programs,” pp. 394–408, 2025, doi: 10.1007/978-981-96-7005-5_27
Source
Communications in Computer and Information Science
Conference
31st International Conference on Neural Information Processing, ICONIP 2024
Keywords
Binary programs, Blockchain, Code execution vulnerabilities, Deep learning, Smart contract
Subjects
Source
31st International Conference on Neural Information Processing, ICONIP 2024
Publisher
Springer Nature