Open AccessDeep Program Representation Learning Analysis for Program Security
Author(s) -
Na Li,
Haoyu Zhang,
Zhihui Hu,
Guang Kou,
Huadong Dai
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1971/1/012098
Subject(s) - computer science , deep learning , representation (politics) , artificial intelligence , malware , program analysis , vulnerability (computing) , software security assurance , feature learning , data science , machine learning , information security , computer security , programming language , security service , politics , political science , law
As the scale of software and the complexity of programs continue to grow, it is hard to meet development of modern computer technology only by manually extracting program features. In recent years, deep learning has achieved rapid development in different fields. Program representation learning based on deep learning has been widely used in many works, such as software vulnerability analysis, program analysis and malware detection. And it has gradually become a hot research direction in information security. After the deep analysis of existing research work on automatic program security detection, we catalog deep program representation learning for program security based on data representation and provide a comprehensive overview of deep program representation learning for program security under different application scenes. Then, we propose a deep program representation learning framework for program security. Finally, we conduct comparative analysis and summarize the challenges in deep program representation learning for program security.