When Less is Enough: Positive and Unlabeled Learning Model for Vulnerability Detection

Automated code vulnerability detection has gained increasing attention in recent years. The deep learning (DL)-based methods, which implicitly learn vulnerable code patterns, have proven effective in vulnerability detection. The performance of DL-based methods usually relies on the quantity and qual...

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Vydané v:	IEEE/ACM International Conference on Automated Software Engineering : [proceedings] s. 345 - 357
Hlavní autori:	Wen, Xin-Cheng, Wang, Xinchen, Gao, Cuiyun, Wang, Shaohua, Liu, Yang, Gu, Zhaoquan
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 11.09.2023
Predmet:	Benchmark testing Codes Deep learning Measurement positive and unlabeled learning Prototypes Representation learning Software vulnerability detection source code representation Source coding
ISSN:	2643-1572
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