# Malware Detection Using Dynamic Birthmarks

arXiv: https://arxiv.org/abs/1901.07312 · HTML: https://mlsanity.com/paper/1901.07312/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jan 2019.

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

- [Malware Classification with Word Embedding Features](https://mlsanity.com/paper/2103.02711/) (2021)

## Abstract (arXiv)

In this paper, we explore the effectiveness of dynamic analysis techniques for identifying malware, using Hidden Markov Models (HMMs) and Profile Hidden Markov Models (PHMMs), both trained on sequences of API calls. We contrast our results to static analysis using HMMs trained on sequences of opcodes, and show that dynamic analysis achieves significantly stronger results in many cases. Furthermore, in contrasting our two dynamic analysis techniques, we find that using PHMMs consistently outperforms our analysis based on HMMs.