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Found 8 papers, 6 papers with code
Are Latent Vulnerabilities Hidden Gems for Software Vulnerability Prediction? An Empirical Study
To bridge these gaps, we conduct a large-scale study on the latent vulnerable functions in two commonly used SV datasets and their utilization for function-level and line-level SV predictions.
Towards an Improved Understanding of Software Vulnerability Assessment Using Data-Driven Approaches
The thesis advances the field of software security by providing knowledge and automation support for software vulnerability assessment using data-driven approaches.
On the Use of Fine-grained Vulnerable Code Statements for Software Vulnerability Assessment Models
We show that vulnerable statements are 5. 8 times smaller in size, yet exhibit 7. 5-114. 5% stronger assessment performance (Matthews Correlation Coefficient (MCC)) than non-vulnerable statements.
Automated Security Assessment for the Internet of Things
This security model automatically assesses the security of the IoT network by capturing potential attack paths.
DeepCVA: Automated Commit-level Vulnerability Assessment with Deep Multi-task Learning
It is increasingly suggested to identify Software Vulnerabilities (SVs) in code commits to give early warnings about potential security risks.
A Survey on Data-driven Software Vulnerability Assessment and Prioritization
Software Vulnerabilities (SVs) are increasing in complexity and scale, posing great security risks to many software systems.
PUMiner: Mining Security Posts from Developer Question and Answer Websites with PU Learning
Using PUMiner, we provide the largest and up-to-date security content on Q&A websites for practitioners and researchers.
Deep Learning for Source Code Modeling and Generation: Models, Applications and Challenges
Deep Learning (DL) techniques for Natural Language Processing have been evolving remarkably fast.
