README.md

---
language:
- en
license: mit
tags:
- knowledge-graph
- rdf
- owl
- ontology
- cybersecurity
annotations_creators:
- expert-generated
pretty_name: D3FEND
size_categories:
- 100K<n<1M
task_categories:
- graph-ml
dataset_info:
  features:
    - name: subject
      dtype: string
    - name: predicate
      dtype: string
    - name: object
      dtype: string
  config_name: default
  splits:
    - name: train
      num_bytes: 46899451
      num_examples: 231842
  dataset_size: 46899451
viewer: false
---

# D3FEND: A knowledge graph of cybersecurity countermeasures

### Overview
D3FEND encodes a countermeasure knowledge base in the form of a
knowledge graph. It meticulously organizes key concepts and relations
in the cybersecurity countermeasure domain, linking each to pertinent
references in the cybersecurity literature. 

### Use-cases
Researchers and cybersecurity enthusiasts can leverage D3FEND to:
- Develop sophisticated graph-based models.
- Fine-tune large language models, focusing on cybersecurity knowledge
  graph completion.
- Explore the complexities and nuances of defensive techniques,
  mappings to MITRE ATT&CK, weaknesses (CWEs), and cybersecurity
  taxonomies.
- Gain insight into ontology development and modeling in the
  cybersecurity domain.

### Preprocessing

### Source:
- [Dataset Repository - 0.13.0-BETA-1](https://github.com/d3fend/d3fend-ontology/tree/release/0.13.0-BETA-1)
- [Commit Details](https://github.com/d3fend/d3fend-ontology/commit/3dcc495879bb62cee5c4109e9b784dd4a2de3c9d)
- [CWE Extension](https://github.com/d3fend/d3fend-ontology/tree/release/0.13.0-BETA-1/extensions/cwe)

#### Building and Verification:
1. **Construction**: The ontology, denoted as `d3fend-full.owl`, was
   built from the beta version of the D3FEND ontology referenced
   above using documented README in d3fend-ontology. This includes the
   CWE extensions. 
2. **Importation and Reasoning**: Imported into Protege version 5.6.1,
   utilizing the Pellet reasoner plugin for logical reasoning and
   verification.
3. **Coherence Check**: Utilized the Debug Ontology plugin in Protege
   to ensure the ontology's coherence and consistency.

#### Exporting, Transformation, and Compression:
Note: The following steps were performed using Apache Jena's command
line tools. (https://jena.apache.org/documentation/tools/)
1. **Exporting Inferred Axioms**: Post-verification, I exported
   inferred axioms along with asserted axioms and
   annotations. [Detailed
   Process](https://www.michaeldebellis.com/post/export-inferred-axioms)
2. **Filtering**: The materialized ontology was filtered using
   `d3fend.rq` to retain relevant triples.
3. **Format Transformation**: Subsequently transformed to Turtle and
   N-Triples formats for diverse usability. Note: I export in Turtle
   first because it is easier to read and verify. Then I convert to
   N-Triples.
   ```shell
   arq --query=d3fend.rq --data=d3fend.owl --results=turtle > d3fend.ttl
   riot --output=nt d3fend.ttl > d3fend.nt
   ```
4. **Compression**: Compressed the resulting ontology files using
   gzip.

### How to Load the Dataset:

You can load this dataset using the Hugging Face Datasets library with
the following Python code:

```python
from datasets import load_dataset
dataset = load_dataset('wikipunk/d3fend', split='train')
```

### Acknowledgements
This ontology is developed by MITRE Corporation and is licensed under
the MIT license. I would like to thank the authors for their work
which has opened my eyes to a new world of cybersecurity modeling.

If you are a cybersecurity expert please consider [contributing to
D3FEND](https://d3fend.mitre.org/contribute/).