# Applications of Positive Unlabeled (PU) and Negative Unlabeled (NU) Learning in Cybersecurity

arXiv: https://arxiv.org/abs/2412.06203 · HTML: https://mlsanity.com/paper/2412.06203/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 22 references that are in the map.
- First posted on arXiv 9 Dec 2024.

## Builds on (most cited first, 3 of 3)

- [NURD: Negative-Unlabeled Learning for Online Datacenter Straggler Prediction](https://mlsanity.com/paper/2203.08339/) (2022)
- [Meta-learning for Positive-unlabeled Classification](https://mlsanity.com/paper/2406.03680/) (2024)
- [Semi-Supervised Classification Based on Classification from Positive and Unlabeled Data](https://mlsanity.com/paper/1605.06955/) (2016)

## Abstract (arXiv)

This paper explores the relatively underexplored application of Positive Unlabeled (PU) Learning and Negative Unlabeled (NU) Learning in the cybersecurity domain. While these semi-supervised learning methods have been applied successfully in fields like medicine and marketing, their potential in cybersecurity remains largely untapped. The paper identifies key areas of cybersecurity--such as intrusion detection, vulnerability management, malware detection, and threat intelligence--where PU/NU learning can offer significant improvements, particularly in scenarios with imbalanced or limited labeled data. We provide a detailed problem formulation for each subfield, supported by mathematical reasoning, and highlight the specific challenges and research gaps in scaling these methods to real-time systems, addressing class imbalance, and adapting to evolving threats. Finally, we propose future directions to advance the integration of PU/NU learning in cybersecurity, offering solutions that can better detect, manage, and mitigate emerging cyber threats.