Abstract
Vulnerability identification is crucial to protect the software systems from attacks for cyber security. It is especially important to localize the vulnerable functions among the source code to facilitate the fix. However, it is a challenging and tedious process, and also requires specialized security expertise. Inspired by the work on manually-defined patterns of vulnerabilities from various code representation graphs and the recent advance on graph neural networks, we propose Devign, a general graph neural network based model for graph-level classification through learning on a rich set of code semantic representations. It includes a novel Conv module to efficiently extract useful features in the learned rich node representations for graph-level classification. The model is trained over manually labeled datasets built on 4 diversified large-scale open-source C projects that incorporate high complexity and variety of real source code instead of synthesis code used in previous works. The results of the extensive evaluation on the datasets demonstrate that Devign outperforms the state of the arts significantly with an average of 10.51% higher accuracy and 8.68% F1 score, increases averagely 4.66% accuracy and 6.37% F1 by the Conv module.
Original language | English |
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Title of host publication | NIPS Proceedings - Advances in Neural Information Processing Systems 32 (NIPS 2019) |
Editors | H. Wallach, H. Larochelle, A. Beygelzimer, F. d'AlcheBuc, E. Fox, R. Garnett |
Place of Publication | San Diego CA USA |
Publisher | Neural Information Processing Systems (NIPS) |
Number of pages | 11 |
Volume | 32 |
Publication status | Published - 2019 |
Externally published | Yes |
Event | Advances in Neural Information Processing Systems 2019 - Vancouver, Canada Duration: 8 Dec 2019 → 14 Dec 2019 Conference number: 32nd https://nips.cc/Conferences/2019 (Proceedings) https://papers.nips.cc/book/advances-in-neural-information-processing-systems-32-2019 (Proceedings) |
Publication series
Name | Advances in Neural Information Processing Systems |
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Publisher | Morgan Kaufmann Publishers |
ISSN (Print) | 1049-5258 |
Conference
Conference | Advances in Neural Information Processing Systems 2019 |
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Abbreviated title | NIPS 2019 |
Country/Territory | Canada |
City | Vancouver |
Period | 8/12/19 → 14/12/19 |
Internet address |