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.
@article{arxiv.2412.06203,
title = {Applications of Positive Unlabeled (PU) and Negative Unlabeled (NU) Learning in Cybersecurity},
author = {Robert Dilworth and Charan Gudla},
journal= {arXiv preprint arXiv:2412.06203},
year = {2024}
}