Multi-view Pre-trained Model for Code Vulnerability Identification
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Xiao, Yinhao
Wang, Jun
Zhang, Wei
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Wang, L
Segal, M
Chen, J
Qiu, T
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Dalian, China
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Abstract
Vulnerability identification is crucial for cyber security in the software-related industry. Early identification methods require significant manual efforts in crafting features or annotating vulnerable code. Although the recent pre-trained models alleviate this issue, they overlook the multiple rich structural information contained in the code itself. In this paper, we propose a novel Multi-View Pre-Trained Model (MV-PTM) that encodes both sequential and multi-type structural information of the source code and uses contrastive learning to enhance code representations. The experiments conducted on two public datasets demonstrate the superiority of MV-PTM. In particular, MV-PTM improves GraphCodeBERT by 3.36% on average in terms of F1 score.
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Wireless Algorithms, Systems, and Applications: 17th International Conference, WASA 2022, Dalian, China, November 24–26, 2022, Proceedings, Part III
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13473
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© 2022 Springer Cham. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher.The original publication is available at www.springerlink.com
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Subject
Machine learning
System and network security
CLONE DETECTION
Computer Science
Computer Science, Artificial Intelligence
Contrastive learning
Pre-trained model
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Citation
Jiang, X; Xiao, Y; Wang, J; Zhang, W, Multi-view Pre-trained Model for Code Vulnerability Identification, Wireless Algorithms, Systems, and Applications: 17th International Conference, WASA 2022, Dalian, China, November 24–26, 2022, Proceedings, Part III, 2022, 13473, pp. 127-135