OutCenTR: A novel semi-supervised framework for predicting exploits of vulnerabilities in high-dimensional datasets. (arXiv:2304.10511v1 [cs.CR])

An ever-growing number of vulnerabilities are reported every day. Yet these
vulnerabilities are not all the same; Some are more targeted than others.
Correctly estimating the likelihood of a vulnerability being exploited is a
critical task for system administrators. This aids the system administrators in
prioritizing and patching the right vulnerabilities. Our work makes use of
outlier detection techniques to predict vulnerabilities that are likely to be
exploited in highly imbalanced and high-dimensional datasets such as the
National Vulnerability Database. …

administrators critical database datasets detection exploited exploits framework high national national vulnerability database novel patching predict system system administrators task techniques vulnerabilities vulnerability vulnerability database work