# Common Vulnerability Scoring System Prediction based on Open Source Intelligence Information Sources

arXiv: https://arxiv.org/abs/2210.02143 · HTML: https://mlsanity.com/paper/2210.02143/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 42 references that are in the map.
- First posted on arXiv 5 Oct 2022.

## Builds on (most cited first, 7 of 7)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [A Survey on Data Augmentation for Text Classification](https://mlsanity.com/paper/2107.03158/) (2021)
- [A Survey on Data-driven Software Vulnerability Assessment and Prioritization](https://mlsanity.com/paper/2107.08364/) (2021)
- [CVSS-BERT: Explainable Natural Language Processing to Determine the Severity of a Computer Security Vulnerability from its Description](https://mlsanity.com/paper/2111.08510/) (2021)
- [A Look at the Time Delays in CVSS Vulnerability Scoring](https://mlsanity.com/paper/1801.00938/) (2018)

## Built on by (most cited first, 1 of 1)

- [A Risk Manager for Intrusion Tolerant Systems: Enhancing HAL 9000 with New Scoring and Data Sources](https://mlsanity.com/paper/2508.13364/) (2025)

## Abstract (arXiv)

The number of newly published vulnerabilities is constantly increasing. Until now, the information available when a new vulnerability is published is manually assessed by experts using a Common Vulnerability Scoring System (CVSS) vector and score. This assessment is time consuming and requires expertise. Various works already try to predict CVSS vectors or scores using machine learning based on the textual descriptions of the vulnerability to enable faster assessment. However, for this purpose, previous works only use the texts available in databases such as National Vulnerability Database. With this work, the publicly available web pages referenced in the National Vulnerability Database are analyzed and made available as sources of texts through web scraping. A Deep Learning based method for predicting the CVSS vector is implemented and evaluated. The present work provides a classification of the National Vulnerability Database's reference texts based on the suitability and crawlability of their texts. While we identified the overall influence of the additional texts is negligible, we outperformed the state-of-the-art with our Deep Learning prediction models.