VDCRL:通过监督的对比代码表示学习来检测漏洞
Xinghang Lv1, Jianming Fu1, Yu Nie1
1The Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan, 430000, Hubei, China.
概括
本研究介绍了VDCRL,这是一个新的代码漏洞检测框架,可以提高概括性. VDCRL使用监督对比学习和数据增强来增强跨不同数据集的软件安全性.
科学领域:
- 计算机科学 计算机科学
- 软件工程 软件工程 软件工程
- 网络安全 网络安全
背景情况:
- 对于代码漏洞检测的深度学习模型表现有希望,但在将其推广到新数据集时遇到了困难.
- 现有的方法往往过度适应训练数据,导致在看不见的代码上显著降低性能.
- 改进的概括对于自动化漏洞检测的有效现实应用至关重要.
研究的目的:
- 提出VDCRL,这是一个新的代码漏洞检测框架.
- 提高深度学习模型在识别软件漏洞方面的概括能力.
- 在合成和现实数据集上实现卓越的检测性能.
主要方法:
- VDCRL使用监督的对比代码表示学习.
- 基于输入和基于特征空间的数据增强产生了多样化的代码样本.
- 功能融合编码器 (SAFE) 集成了源代码和组装指令功能.
- 用于漏洞检测的是一种双向门式反复单元 (BGRU) 模型.
主要成果:
- 与最先进的方法相比,VDCRL显示了显著改善的泛化能力.
- 该框架在真实数据集上实现了优越的漏洞检测性能.
- 功能融合和监督对比学习是提高性能的关键因素.
结论:
- VDCRL提供了一个强大的解决方案,用于代码漏洞检测,并增强了泛化.
- 拟议的框架有效地解决了当前深度学习方法的局限性.
- 通过改进的漏洞检测,VDCRL在确保软件安全方面取得了重大进展.
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