A systematic literature review on automated software vulnerability detection using machine learning
In recent years, numerous Machine Learning (ML) models, including Deep Learning (DL)
and classic ML models, have been developed to detect software vulnerabilities. However …
and classic ML models, have been developed to detect software vulnerabilities. However …
LineVD: statement-level vulnerability detection using graph neural networks
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 …
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?
Traditional vulnerability detection methods have limitations due to their need for extensive
manual labor. Using automated means for vulnerability detection has attracted research …
manual labor. Using automated means for vulnerability detection has attracted research …
MVD: memory-related vulnerability detection based on flow-sensitive graph neural networks
Memory-related vulnerabilities constitute severe threats to the security of modern software.
Despite the success of deep learning-based approaches to generic vulnerability detection …
Despite the success of deep learning-based approaches to generic vulnerability detection …
Path-sensitive code embedding via contrastive learning for software vulnerability detection
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 …
range of application domains. Recently, much effort has been expended on applying deep …
Vulcnn: An image-inspired scalable vulnerability detection system
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 …
widely used to detect source code vulnerability. To achieve scalable vulnerability scanning …
Vulberta: Simplified source code pre-training for vulnerability detection
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 …
in source code. Our approach pre-trains a RoBERTa model with a custom tokenisation …
An empirical study of deep learning models for vulnerability detection
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 …
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
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 …
model. Corder is designed to alleviate the need of labeled data for code retrieval and code …
Fault-aware neural code rankers
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 …
for various programming tasks. In many instances, LLMs can generate a correct program for …