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Datasets:
wikipunk
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d3fend

Tasks:
Graph Machine Learning
Languages:
English
Size:
100K<n<1M
Tags:
knowledge-graph
rdf
owl
ontology
cybersecurity
License:
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metadata
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. This robust representation offers a detailed insight into the complexities of cybersecurity countermeasures.

Use-cases

Researchers can leverage this dataset to develop graph-based learning models, fine-tune language models for cybersecurity knowledge graph completion, or explore the intricate realm of ontologies in cybersecurity, gaining insights into the complexities and nuances of cybersecurity countermeasures and their implications with their own datasets.

Preprocessing

Source:

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:

  1. Exporting Inferred Axioms: Post-verification, I exported inferred axioms along with asserted axioms and annotations. Detailed Process
  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.
    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:

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.