# Practical Attacks on Machine Learning: A Case Study on Adversarial Windows Malware

arXiv: https://arxiv.org/abs/2207.05548 · HTML: https://mlsanity.com/paper/2207.05548/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 19 references that are in the map.
- First posted on arXiv 12 Jul 2022.

## Builds on (most cited first, 8 of 8)

- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Evasion Attacks against Machine Learning at Test Time](https://mlsanity.com/paper/1708.06131/) (2017)
- [Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning](https://mlsanity.com/paper/1712.03141/) (2017)
- [Audio Adversarial Examples: Targeted Attacks on Speech-to-Text](https://mlsanity.com/paper/1801.01944/) (2018)
- [Functionality-preserving Black-box Optimization of Adversarial Windows Malware](https://mlsanity.com/paper/2003.13526/) (2020)
- [Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detection](https://mlsanity.com/paper/1704.08996/) (2017)
- [Malware Makeover: Breaking ML-based Static Analysis by Modifying Executable Bytes](https://mlsanity.com/paper/1912.09064/) (2019)
- [Deceiving End-to-End Deep Learning Malware Detectors using Adversarial Examples](https://mlsanity.com/paper/1802.04528/) (2018)

## Built on by (most cited first, 2 of 2)

- [A Comparison of Adversarial Learning Techniques for Malware Detection](https://mlsanity.com/paper/2308.09958/) (2023)
- [On the Effectiveness of Adversarial Samples against Ensemble Learning-based Windows PE Malware Detectors](https://mlsanity.com/paper/2309.13841/) (2023)

## Abstract (arXiv)

While machine learning is vulnerable to adversarial examples, it still lacks systematic procedures and tools for evaluating its security in different application contexts. In this article, we discuss how to develop automated and scalable security evaluations of machine learning using practical attacks, reporting a use case on Windows malware detection.