Using a Collated Cybersecurity Dataset for Machine Learning and Artificial Intelligence
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Abstract (arXiv)
Artificial Intelligence (AI) and Machine Learning (ML) algorithms can support the span of indicator-level, e.g. anomaly detection, to behavioral level cyber security modeling and inference. This contribution is based on a dataset named BRON which is amalgamated from public threat and vulnerability behavioral sources. We demonstrate how BRON can support prediction of related threat techniques and attack patterns. We also discuss other AI and ML uses of BRON to exploit its behavioral knowledge.
Key facts. Cited by 2 papers in the map (data through 13 Oct 2026). Builds on 5 of its 16 references that are in the map. First posted on arXiv 5 Aug 2021.
Builds on
5 of 16 references.
References · 5
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,304
- 2021Prospective Artificial Intelligence Approaches for Active Cyber Defencecited by 11
- 2020Linking Threat Tactics, Techniques, and Patterns with Defensive Weaknesses, Vulnerabilities and Affected Platform Configurations for Cyber Huntingcited by 4
- 2020Adversarial Genetic Programming for Cyber Security: A Rising Application Domain Where GP Matterscited by 3
- 2021Evidential Cyber Threat Huntingcited by 1
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Citing papers per year: 2021: 1; 2022: 0; 2023: 1.
2 papers.