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Abundant Modalities Offer More Nutrients: Multi-Modal-Based Function-Level Vulnerability Detection
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DOI：10.1145/3731557.png)
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Abstract
En 中文
Software vulnerabilities are weaknesses in software systems that can lead to significant cybersecurity risks. Recently, several deep learning (DL)-based approaches have been proposed to detect vulnerabilities at the function level. These approaches typically utilize one or a few different modalities (e.g., text representation and graph-based representation) of the function, and have shown promising performance. However, existing studies have not fully leveraged diverse modalities, particularly those that use images to represent functions for vulnerability detection. These approaches often fail to make sufficient use of the important graph structure underlying the images. In this article, we propose MVulD+, a multi-modal-based function-level vulnerability detection approach, which fuses multi-modal features of the function (i.e., text representation, graph representation, and image representation) to detect vulnerabilities. Specifically, MVulD+ leverages a pre-trained model (i.e., UniXcoder) to capture the semantic information of the textual source code, uses a graph neural network to extract graph representations, and employs computer vision techniques to obtain image representations while preserving the graph structure of the function. To investigate the effectiveness of MVulD+, we conduct a large-scale experiment by comparing our approach with nine state-of-the-art baselines. Experimental results demonstrate that MVulD+ improves the DL-based baselines by 24.3-125.7%, 5.2-31.4%, 40.6-192.2%, and 22.3-186.9% in terms of F1-score, Accuracy, Precision, and PR-AUC, respectively.
Keywords:
Vulnerability Detection
Computer Vision
Deep Learning
Multi-Modal Code Representations
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