Related papers: LIVABLE: Exploring Long-Tailed Classification of Software Vulnerability Types

LIVABLE: Exploring Long-Tailed Classification of Software Vulnerability Types

URL: http://arxiv.org/abs/2306.06935v1
Date: Mon, 12 Jun 2023 08:14:16 GMT
Title: LIVABLE: Exploring Long-Tailed Classification of Software Vulnerability Types
Authors: Xin-Cheng Wen, Cuiyun Gao, Feng Luo, Haoyu Wang, Ge Li, and Qing Liao
Abstract summary: We propose a Long-taIled software VulnerABiLity typE classification approach, called LIVABLE. LIVABLE consists of two modules, including (1) vulnerability representation learning module, which improves the propagation steps in GNN. A sequence-to-sequence model is also involved to enhance the vulnerability representations.
Score: 18.949810432641772
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Prior studies generally focus on software vulnerability detection and have demonstrated the effectiveness of Graph Neural Network (GNN)-based approaches for the task. Considering the various types of software vulnerabilities and the associated different degrees of severity, it is also beneficial to determine the type of each vulnerable code for developers. In this paper, we observe that the distribution of vulnerability type is long-tailed in practice, where a small portion of classes have massive samples (i.e., head classes) but the others contain only a few samples (i.e., tail classes). Directly adopting previous vulnerability detection approaches tends to result in poor detection performance, mainly due to two reasons. First, it is difficult to effectively learn the vulnerability representation due to the over-smoothing issue of GNNs. Second, vulnerability types in tails are hard to be predicted due to the extremely few associated samples.To alleviate these issues, we propose a Long-taIled software VulnerABiLity typE classification approach, called LIVABLE. LIVABLE mainly consists of two modules, including (1) vulnerability representation learning module, which improves the propagation steps in GNN to distinguish node representations by a differentiated propagation method. A sequence-to-sequence model is also involved to enhance the vulnerability representations. (2) adaptive re-weighting module, which adjusts the learning weights for different types according to the training epochs and numbers of associated samples by a novel training loss.