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README.md
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- ontology
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- cybersecurity
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annotations_creators:
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- crowdsourced
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- expert-generated
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pretty_name: D3FEND
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size_categories:
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viewer: false
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---
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# D3FEND
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https://github.com/d3fend/d3fend-ontology/tree/release/0.13.0-BETA-1/extensions/cwe
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```
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```
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- ontology
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- cybersecurity
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annotations_creators:
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- expert-generated
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pretty_name: D3FEND
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size_categories:
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viewer: false
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---
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# D3FEND
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A knowledge graph of cybersecurity countermeasures
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### Overview
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D3FEND encodes a countermeasure knowledge base in the form of a
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knowledge graph. It meticulously organizes key concepts and relations
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in the cybersecurity countermeasure domain, linking each to pertinent
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references in the cybersecurity literature. This robust representation
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offers a detailed insight into the complexities of cybersecurity
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countermeasures.
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### Use-cases
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Researchers can leverage this dataset to develop graph-based learning
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models, fine-tune language models for cybersecurity knowledge graph
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completion, or explore the intricate realm of ontologies in
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cybersecurity, gaining insights into the complexities and nuances of
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cybersecurity countermeasures and their implications with their own
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datasets.
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### Preprocessing
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### Source:
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- [Dataset Repository - 0.13.0-BETA-1](https://github.com/d3fend/d3fend-ontology/tree/release/0.13.0-BETA-1)
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- [Commit Details](https://github.com/d3fend/d3fend-ontology/commit/3dcc495879bb62cee5c4109e9b784dd4a2de3c9d)
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- [CWE Extension](https://github.com/d3fend/d3fend-ontology/tree/release/0.13.0-BETA-1/extensions/cwe)
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#### Building and Verification:
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1. **Construction**: The ontology, denoted as `d3fend-full.owl`, was
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built from the beta version of the D3FEND ontology referenced
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above using documented README in d3fend-ontology. This includes the
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CWE extensions.
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2. **Importation and Reasoning**: Imported into Protege version 5.6.1,
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utilizing the Pellet reasoner plugin for logical reasoning and
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verification.
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3. **Coherence Check**: Utilized the Debug Ontology plugin in Protege
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to ensure the ontology's coherence and consistency.
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#### Exporting, Transformation, and Compression:
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1. **Exporting Inferred Axioms**: Post-verification, I exported
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inferred axioms along with asserted axioms and
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annotations. [Detailed
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Process](https://www.michaeldebellis.com/post/export-inferred-axioms)
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2. **Filtering**: The materialized ontology was filtered using
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`d3fend.rq` to retain relevant triples.
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3. **Format Transformation**: Subsequently transformed to Turtle and
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N-Triples formats for diverse usability.
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```shell
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arq --query=d3fend.rq --data=d3fend.owl --results=turtle > d3fend.ttl
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riot --output=nt d3fend.ttl > d3fend.nt
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```
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4. **Compression**: Compressed the resulting ontology files using
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gzip.
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### How to Load the Dataset:
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You can load this dataset using the Hugging Face Datasets library with
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the following Python code:
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```python
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from datasets import load_dataset
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dataset = load_dataset('wikipunk/d3fend', split='train')
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```
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### Acknowledgements
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This ontology is developed by MITRE Corporation and is licensed under
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the MIT license. I would like to thank the authors for their work
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which has opened my eyes to a new world of cybersecurity modeling.
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If you are a cybersecurity expert please consider [contributing to
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D3FEND](https://d3fend.mitre.org/contribute/).
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requirements.txt
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rdflib>=6.0.0
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