Devign: effective vulnerability identification by learning comprehensive program semantics via graph neural networks

Yaqin Zhou, Shangqing Liu, Jingkai Siow, Xiaoning Du, Yang Liu

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

18 Citations (Scopus)

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 languageEnglish
Title of host publicationNIPS Proceedings - Advances in Neural Information Processing Systems 32 (NIPS 2019)
EditorsH. Wallach, H. Larochelle, A. Beygelzimer, F. d'AlcheBuc, E. Fox, R. Garnett
Place of PublicationSan Diego CA USA
PublisherNeural Information Processing Systems (NIPS)
Number of pages11
Volume32
Publication statusPublished - 2019
Externally publishedYes
EventAdvances in Neural Information Processing Systems 2019 - Vancouver, Canada
Duration: 8 Dec 201914 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

NameAdvances in Neural Information Processing Systems
PublisherMorgan Kaufmann Publishers
ISSN (Print)1049-5258

Conference

ConferenceAdvances in Neural Information Processing Systems 2019
Abbreviated titleNIPS 2019
CountryCanada
CityVancouver
Period8/12/1914/12/19
Internet address

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