# Machine Learning in Cyber-Security - Problems, Challenges and Data Sets

arXiv: https://arxiv.org/abs/1812.07858 · HTML: https://mlsanity.com/paper/1812.07858/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Dec 2018.

## Built on by (most cited first, 5 of 5)

- [Follow Your Nose -- Which Code Smells are Worth Chasing?](https://mlsanity.com/paper/2103.01861/) (2021)
- [Mapping the Internet: Modelling Entity Interactions in Complex Heterogeneous Networks](https://mlsanity.com/paper/2104.09650/) (2021)
- [ComSum: Commit Messages Summarization and Meaning Preservation](https://mlsanity.com/paper/2108.10763/) (2021)
- [A Comparative Analysis of DNN-based White-Box Explainable AI Methods in Network Security](https://mlsanity.com/paper/2501.07801/) (2025)
- [Fast likelihood-based change point detection](https://mlsanity.com/paper/2301.08892/) (2023)

## Abstract (arXiv)

We present cyber-security problems of high importance. We show that in order to solve these cyber-security problems, one must cope with certain machine learning challenges. We provide novel data sets representing the problems in order to enable the academic community to investigate the problems and suggest methods to cope with the challenges. We also present a method to generate labels via pivoting, providing a solution to common problems of lack of labels in cyber-security.