# Using a Collated Cybersecurity Dataset for Machine Learning and Artificial Intelligence

arXiv: https://arxiv.org/abs/2108.02618 · HTML: https://mlsanity.com/paper/2108.02618/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 16 references that are in the map.
- First posted on arXiv 5 Aug 2021.

## Builds on (most cited first, 5 of 5)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Prospective Artificial Intelligence Approaches for Active Cyber Defence](https://mlsanity.com/paper/2104.09981/) (2021)
- [Linking Threat Tactics, Techniques, and Patterns with Defensive Weaknesses, Vulnerabilities and Affected Platform Configurations for Cyber Hunting](https://mlsanity.com/paper/2010.00533/) (2020)
- [Adversarial Genetic Programming for Cyber Security: A Rising Application Domain Where GP Matters](https://mlsanity.com/paper/2004.04647/) (2020)
- [Evidential Cyber Threat Hunting](https://mlsanity.com/paper/2104.10319/) (2021)

## Built on by (most cited first, 2 of 2)

- [LLMs Killed the Script Kiddie: How Agents Supported by Large Language Models Change the Landscape of Network Threat Testing](https://mlsanity.com/paper/2310.06936/) (2023)
- [Towards automation of threat modeling based on a semantic model of attack patterns and weaknesses](https://mlsanity.com/paper/2112.04231/) (2021)

## 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.