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. We propose a dimensionality reduction technique, OutCenTR, that enhances the baseline outlier detection models. We further demonstrate the effectiveness and efficiency of OutCenTR empirically with 4 benchmark and 12 synthetic datasets. The results of our experiments show on average a 5-fold improvement of F1 score in comparison with state-of-the-art dimensionality reduction techniques such as PCA and GRP.

本研究利用异常检测技术预测易受攻击的漏洞，提出一种OutCenTR降维技术来增强基线异常检测模型，实验证明在高度不平衡和高维度数据集中，与主流的PCA和GRP技术相比，OutCenTR平均提高了5倍的F1得分。

OutCenTR：一种用于高维数据集中预测漏洞利用的新型半监督框架