https://github.com/alan-turing-institute/dynamic_cyber_risk
Science Score: 13.0%
This score indicates how likely this project is to be science-related based on various indicators:
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○CITATION.cff file
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✓codemeta.json file
Found codemeta.json file -
○.zenodo.json file
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○DOI references
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○Academic publication links
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○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (7.2%) to scientific vocabulary
Repository
Basic Info
- Host: GitHub
- Owner: alan-turing-institute
- License: mit
- Language: HTML
- Default Branch: main
- Size: 11 MB
Statistics
- Stars: 3
- Watchers: 4
- Forks: 1
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
Dynamic cyber risk estimation with Competitive Quantile Autoregression
The visualisation of the predictions of the cyber hacking breaches' sizes is available here, and of the predictions of cyber hacking breaches' inter-arrival times is available here. You can also reproduce the results using Binder. Click on 
. You will see the RStudio window. In the RStudio, you can run the notebooks located in the folder notebooks that reproduce the paper's results. The main functions of the implementation are located in the folder dynamic_risk.
Installation
The easiest way to run the project is to use Binder (see above). If you want to run the project locally you need to download R and RStudio.
Clone the repository:
bash
git clone https://github.com/alan-turing-institute/dynamic_cyber_risk.git dynamic_cyber_risk
Open Rstudio and run install.R to install the required packages. After that, you should be able to run the notebooks to reproduce the paper's results.
Owner
- Name: The Alan Turing Institute
- Login: alan-turing-institute
- Kind: organization
- Email: info@turing.ac.uk
- Website: https://turing.ac.uk
- Repositories: 477
- Profile: https://github.com/alan-turing-institute
The UK's national institute for data science and artificial intelligence.
GitHub Events
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Top Authors
Issue Authors
- mhauru (1)