Enhancing Code Vulnerability Detection via Vulnerability-Preserving Data Augmentation

• URL: http://arxiv.org/abs/2404.09599v1
• Date: Mon, 15 Apr 2024 09:10:52 GMT
• Title: Enhancing Code Vulnerability Detection via Vulnerability-Preserving Data Augmentation
• Authors: Shangqing Liu, Wei Ma, Jian Wang, Xiaofei Xie, Ruitao Feng, Yang Liu,
• Abstract summary: Source code vulnerability detection aims to identify inherent vulnerabilities to safeguard software systems from potential attacks. Many prior studies overlook diverse vulnerability characteristics, simplifying the problem into a binary (0-1) classification task. FGVulDet employs multiple classifiers to discern characteristics of various vulnerability types and combines their outputs to identify the specific type of vulnerability. FGVulDet is trained on a large-scale dataset from GitHub, encompassing five different types of vulnerabilities.
• Score: 29.72520866016839
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Source code vulnerability detection aims to identify inherent vulnerabilities to safeguard software systems from potential attacks. Many prior studies overlook diverse vulnerability characteristics, simplifying the problem into a binary (0-1) classification task for example determining whether it is vulnerable or not. This poses a challenge for a single deep learning-based model to effectively learn the wide array of vulnerability characteristics. Furthermore, due to the challenges associated with collecting large-scale vulnerability data, these detectors often overfit limited training datasets, resulting in lower model generalization performance. To address the aforementioned challenges, in this work, we introduce a fine-grained vulnerability detector namely FGVulDet. Unlike previous approaches, FGVulDet employs multiple classifiers to discern characteristics of various vulnerability types and combines their outputs to identify the specific type of vulnerability. Each classifier is designed to learn type-specific vulnerability semantics. Additionally, to address the scarcity of data for some vulnerability types and enhance data diversity for learning better vulnerability semantics, we propose a novel vulnerability-preserving data augmentation technique to augment the number of vulnerabilities. Taking inspiration from recent advancements in graph neural networks for learning program semantics, we incorporate a Gated Graph Neural Network (GGNN) and extend it to an edge-aware GGNN to capture edge-type information. FGVulDet is trained on a large-scale dataset from GitHub, encompassing five different types of vulnerabilities. Extensive experiments compared with static-analysis-based approaches and learning-based approaches have demonstrated the effectiveness of FGVulDet.

Related papers

• DFEPT: Data Flow Embedding for Enhancing Pre-Trained Model Based Vulnerability Detection [7.802093464108404]
  We propose a data flow embedding technique to enhance the performance of pre-trained models in vulnerability detection tasks. Specifically, we parse data flow graphs from function-level source code, and use the data type of the variable as the node characteristics of the DFG. Our research shows that DFEPT can provide effective vulnerability semantic information to pre-trained models, achieving an accuracy of 64.97% on the Devign dataset and an F1-Score of 47.9% on the Reveal dataset.
  arXiv  Detail & Related papers  (2024-10-24T07:05:07Z)
• Enhancing Pre-Trained Language Models for Vulnerability Detection via Semantic-Preserving Data Augmentation [4.374800396968465]
  We propose a data augmentation technique aimed at enhancing the performance of pre-trained language models for vulnerability detection. By incorporating our augmented dataset in fine-tuning a series of representative code pre-trained models, up to 10.1% increase in accuracy and 23.6% increase in F1 can be achieved.
  arXiv  Detail & Related papers  (2024-09-30T21:44:05Z)
• Can An Old Fashioned Feature Extraction and A Light-weight Model Improve Vulnerability Type Identification Performance? [6.423483122892239]
  We investigate the problem of vulnerability type identification (VTI) We evaluate the performance of the well-known and advanced pre-trained models for VTI on a large set of vulnerabilities. We introduce a lightweight independent component to refine the predictions of the baseline approach.
  arXiv  Detail & Related papers  (2023-06-26T14:28:51Z)
• LIVABLE: Exploring Long-Tailed Classification of Software Vulnerability Types [18.949810432641772]
  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.
  arXiv  Detail & Related papers  (2023-06-12T08:14:16Z)
• Learning to Quantize Vulnerability Patterns and Match to Locate Statement-Level Vulnerabilities [19.6975205650411]
  A vulnerability codebook is learned, which consists of quantized vectors representing various vulnerability patterns. During inference, the codebook is iterated to match all learned patterns and predict the presence of potential vulnerabilities. Our approach was extensively evaluated on a real-world dataset comprising more than 188,000 C/C++ functions.
  arXiv  Detail & Related papers  (2023-05-26T04:13:31Z)
• Enhancing Multiple Reliability Measures via Nuisance-extended Information Bottleneck [77.37409441129995]
  In practical scenarios where training data is limited, many predictive signals in the data can be rather from some biases in data acquisition. We consider an adversarial threat model under a mutual information constraint to cover a wider class of perturbations in training. We propose an autoencoder-based training to implement the objective, as well as practical encoder designs to facilitate the proposed hybrid discriminative-generative training.
  arXiv  Detail & Related papers  (2023-03-24T16:03:21Z)
• Improving robustness of jet tagging algorithms with adversarial training [56.79800815519762]
  We investigate the vulnerability of flavor tagging algorithms via application of adversarial attacks. We present an adversarial training strategy that mitigates the impact of such simulated attacks.
  arXiv  Detail & Related papers  (2022-03-25T19:57:19Z)
• VELVET: a noVel Ensemble Learning approach to automatically locate VulnErable sTatements [62.93814803258067]
  This paper presents VELVET, a novel ensemble learning approach to locate vulnerable statements in source code. Our model combines graph-based and sequence-based neural networks to successfully capture the local and global context of a program graph. VELVET achieves 99.6% and 43.6% top-1 accuracy over synthetic data and real-world data, respectively.
  arXiv  Detail & Related papers  (2021-12-20T22:45:27Z)
• Software Vulnerability Detection via Deep Learning over Disaggregated Code Graph Representation [57.92972327649165]
  This work explores a deep learning approach to automatically learn the insecure patterns from code corpora. Because code naturally admits graph structures with parsing, we develop a novel graph neural network (GNN) to exploit both the semantic context and structural regularity of a program.
  arXiv  Detail & Related papers  (2021-09-07T21:24:36Z)
• Information Obfuscation of Graph Neural Networks [96.8421624921384]
  We study the problem of protecting sensitive attributes by information obfuscation when learning with graph structured data. We propose a framework to locally filter out pre-determined sensitive attributes via adversarial training with the total variation and the Wasserstein distance.
  arXiv  Detail & Related papers  (2020-09-28T17:55:04Z)
• Graph Backdoor [53.70971502299977]
  We present GTA, the first backdoor attack on graph neural networks (GNNs) GTA departs in significant ways: it defines triggers as specific subgraphs, including both topological structures and descriptive features. It can be instantiated for both transductive (e.g., node classification) and inductive (e.g., graph classification) tasks.
  arXiv  Detail & Related papers  (2020-06-21T19:45:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.