From text to threats: A language model approach to software vulnerability detection
Author:
Omar Marwan1, Burrell Darrell2
Affiliation:
1. 1 College of Computing, Illinois Institute of Technology , Capitol Technology University , Laurel, Maryland 20708 , USA 2. 2 College of Computing , Capitol Technology University , Laurel, Maryland 20708 , USA
Abstract
Abstract
In the rapidly-evolving landscape of software development, the detection of vulnerabilities in source code has become paramount. Our study introduces a novel knowledge distillation (KD) technique aimed at enhancing vulnerability detection in software codebases. Using benchmark datasets such as SARD, SeVC, Devign, and D2A, we assess the prowess of the KD method when applied to different classifiers, specifically GPT2, CodeBERT, and LSTM. The empirical results revealed a marked improvement in the performance of these classifiers upon the implementation of the KD technique, particularly with the GPT-2 model demonstrating the most promising outcomes. This work underscores the potential of integrating transformer-based learning models, like GPT-2, with knowledge distillation for more efficient and accurate vulnerability detection.
Publisher
Walter de Gruyter GmbH
Reference26 articles.
1. Alharbi A.R., Hijji M., Aljaedi A., Enhancing topic clustering for Arabic security news based on k-means and topic modelling, IET Networks, 10(6), 278-294, 2021. 2. Beyer L., Zhai X., Royer A., Markeeva L., Anil R., Kolesnikov A., Knowledge distillation: A good teacher is patient and consistent, In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 10925-10934, 2022. 3. Furlanello T., Lipton Z., Tschannen M., Itti L., Anandkumar A., Born again neural networks, Proceedings of the 35th International Conference on Machine Learning, 1607-1616, 2018. 4. Xie C., Tan M., Gong B., Wang J., Yuille A.L., Le Q.V., Adversarial examples improve image recognition, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 819-828, 2020. 5. Chen Z., Xie X., Li Y., Luo J., Code representation learning with AST paths and their contexts, In Proceedings of the 27th ACM SIGSOFT International Symposium on Software Testing and Analysis, 312-322, 2019.
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Decision Trees Unleashed;Innovations, Securities, and Case Studies Across Healthcare, Business, and Technology;2024-04-12 2. Service Reliability Based on Fault Prediction and Container Migration in Edge Computing;Applied Sciences;2023-11-30
|
|