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  1. README.md +59 -22
  2. requirements.txt +1 -0
README.md CHANGED
@@ -9,7 +9,6 @@ tags:
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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:
@@ -33,35 +32,73 @@ dataset_info:
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  viewer: false
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  ---
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- # D3FEND
 
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- Branch: https://github.com/d3fend/d3fend-ontology/tree/release/0.13.0-BETA-1
 
 
 
 
 
 
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- Commit: 3dcc495879bb62cee5c4109e9b784dd4a2de3c9d
 
 
 
 
 
 
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- CWE extension:
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- https://github.com/d3fend/d3fend-ontology/tree/release/0.13.0-BETA-1/extensions/cwe
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- After building d3fend-full.owl I imported the ontology into Protege
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- version 5.6.1 with the Pellet reasoner plug-in. First I use the Debug
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- Ontology plugin to check the ontology for consistency and
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- coherency. If it all checks out, I move onto exporting the inferences.
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- In Protege navigate to File>Export Inferred Axioms as ontology and
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- make sure to check all of the checkboxes including asserted axioms and
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- annotations. See this blog post for more
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- information: https://www.michaeldebellis.com/post/export-inferred-axioms.
 
 
 
 
 
 
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- Once you have the materialized ontology you can filter it with
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- d3fend.sparql.
 
 
 
 
 
 
 
 
 
 
 
 
 
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- ``` shell
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- arq --query=d3fend.rq --data=d3fend.owl --results=turtle > d3fend.ttl
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- ```
 
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- ``` shell
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- riot --output=nt d3fend.ttl > d3fend.nt
 
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  ```
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- The d3fend.owl and d3fend.nt files then are compressed using gzip.
 
 
 
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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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+
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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/).
requirements.txt ADDED
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+ rdflib>=6.0.0