A Survey on Data-driven Software Vulnerability Assessment and Prioritization
It helps software security practitioners optimize mitigation plans, but it is incremental as a survey rather than new research.
This survey addresses the problem of assessing and prioritizing software vulnerabilities (SVs) by reviewing data-driven techniques like machine learning and deep learning, and it provides a taxonomy and best practices for practitioners.
Software Vulnerabilities (SVs) are increasing in complexity and scale, posing great security risks to many software systems. Given the limited resources in practice, SV assessment and prioritization help practitioners devise optimal SV mitigation plans based on various SV characteristics. The surges in SV data sources and data-driven techniques such as Machine Learning and Deep Learning have taken SV assessment and prioritization to the next level. Our survey provides a taxonomy of the past research efforts and highlights the best practices for data-driven SV assessment and prioritization. We also discuss the current limitations and propose potential solutions to address such issues.