VulTR: Software vulnerability detection model based on multi-layer key feature enhancement

IF 4.8 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Haitao He , Sheng Wang , Yanmin Wang , Ke Liu , Lu Yu
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引用次数: 0

Abstract

Software vulnerabilities pose a huge threat to current network security, which continues to lead to data leaks and system damage. In order to effectively identify and patch these vulnerabilities, researchers have proposed automated detection methods based on deep learning. However, most of the existing methods only rely on single-dimensional data representation and fail to fully explore the composite characteristics of the code. Among them, the sequence embedding method fails to effectively capture the structural characteristics of the code, while the graph embedding method focuses more on the global characteristics of the overall graph structure and is still insufficient in optimizing the representation of nodes. In view of this, this paper constructs the VulTR model, which incorporates an importance assessment mechanism to strengthen the key syntax levels of the source code (from lexical elements to nodes and graph-level structures), significantly improving the importance of key vulnerability features in classification decisions. At the same time, a relationship connection diagram is constructed to describe the spatial characteristics of the correlations between functions. Experimentally verified, VulTR's F1 scores on both synthetic and real data sets exceed those of the compared models (VulDeePecker, SySeVR, Devign, VulCNN, IVDetect, and mVulPreter).
VulTR:基于多层关键特征增强的软件漏洞检测模型
软件漏洞对当前的网络安全构成了巨大威胁,不断导致数据泄露和系统损坏。为了有效识别和修补这些漏洞,研究人员提出了基于深度学习的自动检测方法。然而,现有方法大多只依赖于单维数据表示,无法充分挖掘代码的复合特性。其中,序列嵌入方法未能有效捕捉代码的结构特征,而图嵌入方法更注重整体图结构的全局特征,在优化节点表示方面仍有不足。有鉴于此,本文构建了 VulTR 模型,该模型结合重要性评估机制,强化了源代码的关键语法层次(从词素到节点和图级结构),显著提高了关键漏洞特征在分类决策中的重要性。同时,还构建了关系连接图来描述函数之间相关性的空间特征。经过实验验证,VulTR 在合成数据集和真实数据集上的 F1 分数都超过了同类模型(VulDeePecker、SySeVR、Devign、VulCNN、IVDetect 和 mVulPreter)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Computers & Security
Computers & Security 工程技术-计算机:信息系统
CiteScore
12.40
自引率
7.10%
发文量
365
审稿时长
10.7 months
期刊介绍: Computers & Security is the most respected technical journal in the IT security field. With its high-profile editorial board and informative regular features and columns, the journal is essential reading for IT security professionals around the world. Computers & Security provides you with a unique blend of leading edge research and sound practical management advice. It is aimed at the professional involved with computer security, audit, control and data integrity in all sectors - industry, commerce and academia. Recognized worldwide as THE primary source of reference for applied research and technical expertise it is your first step to fully secure systems.
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