Software Vulnerability Detection Using Deep Neural Networks: A Survey
The constantly increasing number of disclosed security vulnerabilities have become an important concern in the software industry and in the field of cybersecurity, suggesting that the current approaches for vulnerability detection demand further improvement. The booming of the open-source software c...
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| Vydáno v: | Proceedings of the IEEE Ročník 108; číslo 10; s. 1825 - 1848 |
|---|---|
| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
New York
IEEE
01.10.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Témata: | |
| ISSN: | 0018-9219, 1558-2256 |
| On-line přístup: | Získat plný text |
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| Abstract | The constantly increasing number of disclosed security vulnerabilities have become an important concern in the software industry and in the field of cybersecurity, suggesting that the current approaches for vulnerability detection demand further improvement. The booming of the open-source software community has made vast amounts of software code available, which allows machine learning and data mining techniques to exploit abundant patterns within software code. Particularly, the recent breakthrough application of deep learning to speech recognition and machine translation has demonstrated the great potential of neural models' capability of understanding natural languages. This has motivated researchers in the software engineering and cybersecurity communities to apply deep learning for learning and understanding vulnerable code patterns and semantics indicative of the characteristics of vulnerable code. In this survey, we review the current literature adopting deep-learning-/neural-network-based approaches for detecting software vulnerabilities, aiming at investigating how the state-of-the-art research leverages neural techniques for learning and understanding code semantics to facilitate vulnerability discovery. We also identify the challenges in this new field and share our views of potential research directions. |
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| AbstractList | The constantly increasing number of disclosed security vulnerabilities have become an important concern in the software industry and in the field of cybersecurity, suggesting that the current approaches for vulnerability detection demand further improvement. The booming of the open-source software community has made vast amounts of software code available, which allows machine learning and data mining techniques to exploit abundant patterns within software code. Particularly, the recent breakthrough application of deep learning to speech recognition and machine translation has demonstrated the great potential of neural models' capability of understanding natural languages. This has motivated researchers in the software engineering and cybersecurity communities to apply deep learning for learning and understanding vulnerable code patterns and semantics indicative of the characteristics of vulnerable code. In this survey, we review the current literature adopting deep-learning-/neural-network-based approaches for detecting software vulnerabilities, aiming at investigating how the state-of-the-art research leverages neural techniques for learning and understanding code semantics to facilitate vulnerability discovery. We also identify the challenges in this new field and share our views of potential research directions. |
| Author | Wen, Sheng Han, Qing-Long Lin, Guanjun Zhang, Jun Xiang, Yang |
| Author_xml | – sequence: 1 givenname: Guanjun orcidid: 0000-0003-3280-1307 surname: Lin fullname: Lin, Guanjun email: daniellin1986d@gmail.com organization: School of Information Engineering, Sanming University, Fujian, Sanming, China – sequence: 2 givenname: Sheng orcidid: 0000-0003-0655-666X surname: Wen fullname: Wen, Sheng email: swen@swin.edu.au organization: School of Software and Electrical Engineering, Swinburne University of Technology, Melbourne, VIC, Australia – sequence: 3 givenname: Qing-Long orcidid: 0000-0002-7207-0716 surname: Han fullname: Han, Qing-Long email: qhan@swin.edu.au organization: School of Software and Electrical Engineering, Swinburne University of Technology, Melbourne, VIC, Australia – sequence: 4 givenname: Jun orcidid: 0000-0002-2189-7801 surname: Zhang fullname: Zhang, Jun email: junzhang@swin.edu.au organization: School of Software and Electrical Engineering, Swinburne University of Technology, Melbourne, VIC, Australia – sequence: 5 givenname: Yang orcidid: 0000-0001-5252-0831 surname: Xiang fullname: Xiang, Yang email: yxiang@swin.edu.au organization: School of Software and Electrical Engineering, Swinburne University of Technology, Melbourne, VIC, Australia |
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| SubjectTerms | Artificial neural networks Computer bugs Computer security Cybersecurity Data mining Deep learning deep neural network (DNN) Feature extraction Literature reviews Machine learning machine learning (ML) Machine translation Neural networks Open source software representation learning Semantics Software engineering Software reliability software vulnerability Source code Speech recognition |
| Title | Software Vulnerability Detection Using Deep Neural Networks: A Survey |
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