LCVD: Loop-oriented code vulnerability detection via graph neural network

Mingke Wang

doi:10.1016/j.jss.2023.111706

LCVD: Loop-oriented code vulnerability detection via graph neural network

2023-08-01

4

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AI

DOI：10.1016/j.jss.2023.111706

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Due to the unique mechanism and complex structure, loops in programs can easily lead to various vulnerabilities such as dead loops, memory leaks, resource depletion, etc. Traditional approaches to loop-oriented program analysis (e.g. loop summarization) are costly with a high rate of false positives in complex software systems. To address the issues above, recent works have applied deep learning (DL) techniques to vulnerability detection. However, existing DL-based approaches mainly focused on the general characteristics of most vulnerabilities without considering the semantic information of specific vulnerabilities. As a typical structure in programs, loops are highly iterative with multi-paths. Currently, there is a lack of available approaches to represent loops, as well as useful methods to extract the implicit vulnerability patterns. Therefore, this paper introduces LCVD, an automated loop -oriented code vulnerability detection approach. LCVD represents the source code as the Loop-flow Abstract Syntax Tree (LFAST), which focuses on interleaving multi-paths around loop structures. Then a novel Loop-flow Graph Neural Network (LFGNN) is proposed to learn both the local and overall structure of loop-oriented vulnerabilities. The experimental results demonstrate that LCVD outperforms the three static analysis-based and four state-of-the-art DL-based vulnerability detection approaches across evaluation settings.(c) 2023 Elsevier Inc. All rights reserved.

Keyword:

Loop-oriented vulnerability

Vulnerability detection

Deep learning

Code representation

Graph neural network

LCVD: Loop-oriented code vulnerability detection via graph neural network

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