Enhancing Pre-Trained Language Models for Vulnerability Detection via Semantic-Preserving Data Augmentation

• URL: http://arxiv.org/abs/2410.00249v2
• Date: Thu, 3 Oct 2024 00:37:10 GMT
• Title: Enhancing Pre-Trained Language Models for Vulnerability Detection via Semantic-Preserving Data Augmentation
• Authors: Weiliang Qi, Jiahao Cao, Darsh Poddar, Sophia Li, Xinda Wang,
• Abstract summary: 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.
• Score: 4.374800396968465
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the rapid development and widespread use of advanced network systems, software vulnerabilities pose a significant threat to secure communications and networking. Learning-based vulnerability detection systems, particularly those leveraging pre-trained language models, have demonstrated significant potential in promptly identifying vulnerabilities in communication networks and reducing the risk of exploitation. However, the shortage of accurately labeled vulnerability datasets hinders further progress in this field. Failing to represent real-world vulnerability data variety and preserve vulnerability semantics, existing augmentation approaches provide limited or even counterproductive contributions to model training. In this paper, we propose a data augmentation technique aimed at enhancing the performance of pre-trained language models for vulnerability detection. Given the vulnerability dataset, our method performs natural semantic-preserving program transformation to generate a large volume of new samples with enriched data diversity and variety. By incorporating our augmented dataset in fine-tuning a series of representative code pre-trained models (i.e., CodeBERT, GraphCodeBERT, UnixCoder, and PDBERT), up to 10.1% increase in accuracy and 23.6% increase in F1 can be achieved in the vulnerability detection task. Comparison results also show that our proposed method can substantially outperform other prominent vulnerability augmentation approaches.

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)
• Adversarial Robustification via Text-to-Image Diffusion Models [56.37291240867549]
  Adrial robustness has been conventionally believed as a challenging property to encode for neural networks. We develop a scalable and model-agnostic solution to achieve adversarial robustness without using any data.
  arXiv  Detail & Related papers  (2024-07-26T10:49:14Z)
• Security Vulnerability Detection with Multitask Self-Instructed Fine-Tuning of Large Language Models [8.167614500821223]
  We introduce MSIVD, multitask self-instructed fine-tuning for vulnerability detection, inspired by chain-of-thought prompting and LLM self-instruction. Our experiments demonstrate that MSIVD achieves superior performance, outperforming the highest LLM-based vulnerability detector baseline (LineVul) with a F1 score of 0.92 on the BigVul dataset, and 0.48 on the PreciseBugs dataset.
  arXiv  Detail & Related papers  (2024-06-09T19:18:05Z)
• Enhancing Code Vulnerability Detection via Vulnerability-Preserving Data Augmentation [29.72520866016839]
  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.
  arXiv  Detail & Related papers  (2024-04-15T09:10:52Z)
• FaultGuard: A Generative Approach to Resilient Fault Prediction in Smart Electrical Grids [53.2306792009435]
  FaultGuard is the first framework for fault type and zone classification resilient to adversarial attacks. We propose a low-complexity fault prediction model and an online adversarial training technique to enhance robustness. Our model outclasses the state-of-the-art for resilient fault prediction benchmarking, with an accuracy of up to 0.958.
  arXiv  Detail & Related papers  (2024-03-26T08:51:23Z)
• An Unbiased Transformer Source Code Learning with Semantic Vulnerability Graph [3.3598755777055374]
  Current vulnerability screening techniques are ineffective at identifying novel vulnerabilities or providing developers with code vulnerability and classification. To address these issues, we propose a joint multitasked unbiased vulnerability classifier comprising a transformer "RoBERTa" and graph convolution neural network (GCN) We present a training process utilizing a semantic vulnerability graph (SVG) representation from source code, created by integrating edges from a sequential flow, control flow, and data flow, as well as a novel flow dubbed Poacher Flow (PF)
  arXiv  Detail & Related papers  (2023-04-17T20:54:14Z)
• 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)
• V2W-BERT: A Framework for Effective Hierarchical Multiclass Classification of Software Vulnerabilities [7.906207218788341]
  We present a novel Transformer-based learning framework (V2W-BERT) in this paper. By using ideas from natural language processing, link prediction and transfer learning, our method outperforms previous approaches. We achieve up to 97% prediction accuracy for randomly partitioned data and up to 94% prediction accuracy in temporally partitioned data.
  arXiv  Detail & Related papers  (2021-02-23T05:16:57Z)
• 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)

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.