# Monotonic models for real-time dynamic malware detection

arXiv: https://arxiv.org/abs/1804.03643 · HTML: https://mlsanity.com/paper/1804.03643/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Apr 2018.

## Built on by (most cited first, 1 of 1)

- [Semi-supervised Clustering with Two Types of Background Knowledge: Fusing Pairwise Constraints and Monotonicity Constraints](https://mlsanity.com/paper/2302.14060/) (2023)

## Abstract (arXiv)

In dynamic malware analysis, programs are classified as malware or benign based on their execution logs. We propose a concept of applying monotonic classification models to the analysis process, to make the trained model's predictions consistent over execution time and provably stable to the injection of any noise or `benign-looking' activity into the program's behavior. The predictions of such models change monotonically through the log in the sense that the addition of new lines into the log may only increase the probability of the file being found malicious, which make them suitable for real-time classification on a user's machine. We evaluate monotonic neural network models based on the work by Chistyakovet al. (2017) and demonstrate that they provide stable and interpretable results.