VulSPG: Vulnerability detection based on slice property graph representation learning

Abstract

Vulnerability detection is an important issue in software security. Although various data-driven vulnerability detection methods have been proposed, the task remains challenging since the diversity and complexity of real-world vulnerable code in syntax and semantics make it difficult to extract vulnerable features with regular deep learning models, especially in analyzing a large program. Moreover, the fact that real-world vulnerable codes contain a lot of redundant information unrelated to vulnerabilities will further aggravate the above problem. To mitigate such challenges, we define a novel code representation named Slice Property Graph (SPG), and then propose VulSPG, a new vulnerability detection approach using the improved R-GCN model with triple attention mechanism to identify potential vulnerabilities in SPG. Our approach has at least two advantages over other methods. First, our proposed SPG can reflect the rich semantics and explicit structural information that may be relevance to vulnerabilities, while eliminating as much irrelevant information as possible to reduce the complexity of graph. Second, VulSPG incorporates triple attention mechanism in R-GCNs to achieve more effective learning of vulnerability patterns from SPG. We have extensively evaluated VulSPG on two largescale datasets with programs from SARD and real-world projects. Experimental results prove the effectiveness and efficiency of VulSPG.

Vulnerability Detection With Graph Attention Network And Metric Learning

“…To optimally represent the characteristics of a node in the entire code attribute graph, it is necessary to map the information of its neighbor nodes to itself. Considering that each neighbor node has different weights for its influence, we use a Graph Attention Network with a multi-head attention mechanism; the aggregation effect of the attention mechanism on node information has been demonstrated in many studies [27], [34], [45], [46], [47], [48]. Besides, the attention mechanism can deal with latent noise features.…”

“…Attention-based graph neural network methods are also widely used in vulnerability detection. VulSPG [45] introduced a triple attention mechanism of node attention mechanism, sentence attention mechanism, and subgraph attention mechanism for slice attribute graph to improve the ability of node information aggregation. ACGVD [46] designed a node-level attention mechanism and a pathlevel attention mechanism for data control flow graphs to improve the classification performance.…”

Distilled and Contextualized Neural Models Benchmarked for Vulnerable Function Detection

“…Many researchers argued that the control and data flows are well-structured which can also reveal useful information for understanding code semantics indicative of vulnerable patterns. Therefore, a number of studies utilize Graph Neural Networks (GNNs) for structural information extraction, including FUNDED [42], Devign [3], VulSPG [43], and Zhuang et al [44]. However, these studies highly depend on code analysis to obtain code graph.…”