A systematic literature review on automated software vulnerability detection using machine learning

N Shiri Harzevili, A Boaye Belle, J Wang… - ACM Computing …, 2024 - dl.acm.org
In recent years, numerous Machine Learning (ML) models, including Deep Learning (DL)
and classic ML models, have been developed to detect software vulnerabilities. However …

LineVD: statement-level vulnerability detection using graph neural networks

D Hin, A Kan, H Chen, MA Babar - Proceedings of the 19th international …, 2022 - dl.acm.org
Current machine-learning based software vulnerability detection methods are primarily
conducted at the function-level. However, a key limitation of these methods is that they do …

Interpreters for GNN-based vulnerability detection: Are we there yet?

Y Hu, S Wang, W Li, J Peng, Y Wu, D Zou… - Proceedings of the 32nd …, 2023 - dl.acm.org
Traditional vulnerability detection methods have limitations due to their need for extensive
manual labor. Using automated means for vulnerability detection has attracted research …

MVD: memory-related vulnerability detection based on flow-sensitive graph neural networks

S Cao, X Sun, L Bo, R Wu, B Li, C Tao - Proceedings of the 44th …, 2022 - dl.acm.org
Memory-related vulnerabilities constitute severe threats to the security of modern software.
Despite the success of deep learning-based approaches to generic vulnerability detection …

Path-sensitive code embedding via contrastive learning for software vulnerability detection

X Cheng, G Zhang, H Wang, Y Sui - Proceedings of the 31st ACM …, 2022 - dl.acm.org
Machine learning and its promising branch deep learning have shown success in a wide
range of application domains. Recently, much effort has been expended on applying deep …

Vulcnn: An image-inspired scalable vulnerability detection system

Y Wu, D Zou, S Dou, W Yang, D Xu, H ** - Proceedings of the 44th …, 2022 - dl.acm.org
Since deep learning (DL) can automatically learn features from source code, it has been
widely used to detect source code vulnerability. To achieve scalable vulnerability scanning …

Vulberta: Simplified source code pre-training for vulnerability detection

H Hanif, S Maffeis - 2022 International joint conference on …, 2022 - ieeexplore.ieee.org
This paper presents VulBERTa, a deep learning approach to detect security vulnerabilities
in source code. Our approach pre-trains a RoBERTa model with a custom tokenisation …

An empirical study of deep learning models for vulnerability detection

B Steenhoek, MM Rahman, R Jiles… - 2023 IEEE/ACM 45th …, 2023 - ieeexplore.ieee.org
Deep learning (DL) models of code have recently reported great progress for vulnerability
detection. In some cases, DL-based models have outperformed static analysis tools …

Self-supervised contrastive learning for code retrieval and summarization via semantic-preserving transformations

NDQ Bui, Y Yu, L Jiang - Proceedings of the 44th International ACM …, 2021 - dl.acm.org
We propose Corder, a self-supervised contrastive learning framework for source code
model. Corder is designed to alleviate the need of labeled data for code retrieval and code …

Fault-aware neural code rankers

JP Inala, C Wang, M Yang, A Codas… - Advances in …, 2022 - proceedings.neurips.cc
Large language models (LLMs) have demonstrated an impressive ability to generate code
for various programming tasks. In many instances, LLMs can generate a correct program for …