# Vulnerability Clustering and other Machine Learning Applications of Semantic Vulnerability Embeddings

arXiv: https://arxiv.org/abs/2310.05935 · HTML: https://mlsanity.com/paper/2310.05935/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 33 references that are in the map.
- First posted on arXiv 23 Aug 2023.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015)
- [Exploit Prediction Scoring System (EPSS)](https://mlsanity.com/paper/1908.04856/) (2019)
- [Technical Report -- Expected Exploitability: Predicting the Development of Functional Vulnerability Exploits](https://mlsanity.com/paper/2102.07869/) (2021)
- [An Automated, End-to-End Framework for Modeling Attacks From Vulnerability Descriptions](https://mlsanity.com/paper/2008.04377/) (2020)
- [Probabilistic Approximate Logic and its Implementation in the Logical Imagination Engine](https://mlsanity.com/paper/1907.11321/) (2019)

## Abstract (arXiv)

Cyber-security vulnerabilities are usually published in form of short natural language descriptions (e.g., in form of MITRE's CVE list) that over time are further manually enriched with labels such as those defined by the Common Vulnerability Scoring System (CVSS). In the Vulnerability AI (Analytics and Intelligence) project, we investigated different types of semantic vulnerability embeddings based on natural language processing (NLP) techniques to obtain a concise representation of the vulnerability space. We also evaluated their use as a foundation for machine learning applications that can support cyber-security researchers and analysts in risk assessment and other related activities. The particular applications we explored and briefly summarize in this report are clustering, classification, and visualization, as well as a new logic-based approach to evaluate theories about the vulnerability space.