Software Vulnerability Recognition Task

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A Software Vulnerability Recognition Task is a recognition task for security flaws in software code.



References

2023

2020

  • (Lin, Wen et al., 2020) ⇒ G. Lin, S. Wen, Q.L. Han, J. Zhang, and others. (2020). “Software Vulnerability Detection Using Deep Neural Networks: A Survey." In: IEEE Proceedings.
    • 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.
    • NOTE: It offers a comprehensive review of how deep learning and neural network technologies are being utilized in the field of software vulnerability detection. It discusses the emerging trend of leveraging machine learning methods to understand and identify vulnerable code patterns, and points out current challenges and future research directions in this area.
    • NOTE: It surveys the application of deep neural networks in the field of software vulnerability detection, highlighting their potential advantages.

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