.png){kind=icon}
返回
Learning to Detect Memory-related Vulnerabilities
.png){kind=icon}
PRE
AI
DOI：10.1145/3624744.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Memory-related vulnerabilities can result in performance degradation or even program crashes, constituting severe threats to the security of modern software. Despite the promising results of deep learning (DL)-based vulnerability detectors, there exist three main limitations: (1) rich contextual program semantics related to vulnerabilities have not yet been fully modeled; (2) multi-granularity vulnerability features in hierarchical code structure are still hard to be captured; and (3) heterogeneous flow information is not well utilized. To address these limitations, in this article, we propose a novel DL-based approach, called MVD+, to detect memory-related vulnerabilities at the statement-level. Specifically, it conducts both intraprocedural and interprocedural analysis to model vulnerability features, and adopts a hierarchical representation learning strategy, which performs syntax-aware neural embedding within statements and captures structured context information across statements based on a novel Flow-Sensitive Graph Neural Networks, to learn both syntactic and semantic features of vulnerable code. To demonstrate the performance, we conducted extensive experiments against eight state-of-the-art DL-based approaches as well as five well-known static analyzers on our constructed dataset with 6,879 vulnerabilities in 12 popular C/C++ applications. The experimental results confirmed that MVD+ can significantly outperform current state-of-the-art baselines and make a great trade-off between effectiveness and efficiency.
Keyword:
Statement-level vulnerability detection
abstract syntax tree
graph neural networks
flow analysis
期刊
A
IF:
6.2
论文数:
1.2K
被引数:
3.4K
机构
引用论文
VulDeeLocator: A Deep Learning-Based Fine-Grained Vulnerability DetectorVulDeeLocator: 一种基于深度学习的细粒度漏洞检测器
OAAI
OAAI分享收藏SySeVR: A Framework for Using Deep Learning to Detect Software VulnerabilitiesSySeVR: 使用深度学习检测软件漏洞的框架OAAI
OAAI分享收藏Unearthing the hidden world of roots: Root biomass and architecture differ among species within the same guild.png)
PLOS ONE
OAAI
PLOS ONEIF0
OAAI分享收藏Mapping of spinal interneurons involved in regulation of the lower urinary tract in juvenile male ratsIBRO ReportsOAAI
IBRO ReportsIF0
OAAI分享收藏分享收藏分享收藏
查看更多内容