# OutCenTR: A novel semi-supervised framework for predicting exploits of vulnerabilities in high-dimensional datasets

arXiv: https://arxiv.org/abs/2304.10511 · HTML: https://mlsanity.com/paper/2304.10511/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 33 references that are in the map.
- First posted on arXiv 3 Apr 2023.

## Builds on (most cited first, 5 of 5)

- [Exploit Prediction Scoring System (EPSS)](https://mlsanity.com/paper/1908.04856/) (2019)
- [V2W-BERT: A Framework for Effective Hierarchical Multiclass Classification of Software Vulnerabilities](https://mlsanity.com/paper/2102.11498/) (2021)
- [Early Warnings of Cyber Threats in Online Discussions](https://mlsanity.com/paper/1801.09781/) (2018)
- [Predicting Exploitation of Disclosed Software Vulnerabilities Using Open-source Data](https://mlsanity.com/paper/1707.08015/) (2017)
- [Classifying Web Exploits with Topic Modeling](https://mlsanity.com/paper/1710.05561/) (2017)

## Abstract (arXiv)

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.