# Classifying Web Exploits with Topic Modeling

arXiv: https://arxiv.org/abs/1710.05561 · HTML: https://mlsanity.com/paper/1710.05561/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2017.

## Built on by (most cited first, 2 of 2)

- [A Survey on Data-driven Software Vulnerability Assessment and Prioritization](https://mlsanity.com/paper/2107.08364/) (2021)
- [OutCenTR: A novel semi-supervised framework for predicting exploits of vulnerabilities in high-dimensional datasets](https://mlsanity.com/paper/2304.10511/) (2023)

## Abstract (arXiv)

This short empirical paper investigates how well topic modeling and database meta-data characteristics can classify web and other proof-of-concept (PoC) exploits for publicly disclosed software vulnerabilities. By using a dataset comprised of over 36 thousand PoC exploits, near a 0.9 accuracy rate is obtained in the empirical experiment. Text mining and topic modeling are a significant boost factor behind this classification performance. In addition to these empirical results, the paper contributes to the research tradition of enhancing software vulnerability information with text mining, providing also a few scholarly observations about the potential for semi-automatic classification of exploits in the existing tracking infrastructures.