Software Vulnerability Detection Using Deep Neural Networks: A Survey

By Guanjun Lin, Sheng Wen, Qing-Long Han, Jun Zhang, Yang Xiang. Proceedings of the IEEE, 2020

400 citations recorded by the source corpus.

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.

Subfields: Information Systems, Software, Signal Processing

View Paper: https://www.semanticscholar.org/paper/24220139c25d5e1191e66ed148382dfdcda1486e

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Citations in the corpus

A Survey of Android Malware Detection with Deep Neural Models (2020, background): “…models enhances the learning of abstract and highly non-linear patterns, which helps capture the intrinsic characteristics of complex data [11, 12]; the deep neural network structures allow the features to be learned automatically and with multiple levels of abstraction [13, 14], which…”
Fuzzing: A Survey for Roadmap (2022, background): “Software security is a severe problem of computer systems, which affects people’s daily life and even causes severe financial problems [109, 169].”
CVEfixes: automated collection of vulnerabilities and their fixes from open-source software (2021, background): “[9] emphasize the need for a benchmark for evaluating the effectiveness of ML approaches aimed at fixing security vulnerability in source code.”