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Open Access

Common Vulnerability Scoring System Prediction Based on Open Source Intelligence Information Sources [Data Set & Models]

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Files

Dataset.zip (2.18 GB)
 Models.zip (7.46 GB)

Date

2024-04-23

Type

Other

Authors

Kuehn, Philipp
Relke, David
Reuter, Christian

Journal Title

Journal ISSN

Volume Title

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Abstract

Description

This repository contains the dataset used to train BERT-based models based on open information sources. We aimed to build a more robust model to predict the Common Vulnerability Scoring System (CVSS) score. The repository with the source code used to train and evaluate the models can be found in [this repository](https://github.com/PEASEC/Open-Information-CVSS-Prediction). Please cite the original paper when using this data: "Kuehn, P., Relke, D. N., & Reuter, C. (2023). Common vulnerability scoring system prediction based on open source intelligence information sources. Computers & Security"

Keywords

Common vulnerability scoring system, National vulnerability database, Security management, IT Security, Deep learning

Citation

Identifier

https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/4214

Endorsement

DFG Classification

4.43-04 Künstliche Intelligenz und Maschinelle Lernverfahren
4.43-05 Bild- und Sprachverarbeitung, Computergraphik und Visualisierung, Human Computer Interaction, Ubiquitous und Wearable Computing

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License

MIT License
Except where otherwise noted, this license is described as MIT License