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--- |
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language: |
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- en |
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license: mit |
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tags: |
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- knowledge-graph |
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- rdf |
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- owl |
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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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- 100K<n<1M |
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task_categories: |
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- graph-ml |
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dataset_info: |
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features: |
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- name: subject |
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dtype: string |
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- name: predicate |
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dtype: string |
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- name: object |
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dtype: string |
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config_name: default |
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splits: |
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- name: train |
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num_bytes: 46899451 |
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num_examples: 231842 |
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dataset_size: 46899451 |
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viewer: false |
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--- |
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# D3FEND: 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. |
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### Use-cases |
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Researchers and cybersecurity enthusiasts can leverage D3FEND to: |
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- Develop sophisticated graph-based models. |
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- Fine-tune large language models, focusing on cybersecurity knowledge |
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graph completion. |
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- Explore the complexities and nuances of defensive techniques, |
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mappings to MITRE ATT&CK, weaknesses (CWEs), and cybersecurity |
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taxonomies. |
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- Gain insight into ontology development and modeling in the |
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cybersecurity domain. |
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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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Note: The following steps were performed using Apache Jena's command |
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line tools. (https://jena.apache.org/documentation/tools/) |
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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. Note: I export in Turtle |
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first because it is easier to read and verify. Then I convert to |
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N-Triples. |
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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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