# Best-Effort Adversarial Approximation of Black-Box Malware Classifiers

arXiv: https://arxiv.org/abs/2006.15725 · HTML: https://mlsanity.com/paper/2006.15725/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jun 2020.

## Built on by (most cited first, 4 of 4)

- [I Know What You Trained Last Summer: A Survey on Stealing Machine Learning Models and Defences](https://mlsanity.com/paper/2206.08451/) (2022)
- [Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-Art](https://mlsanity.com/paper/2112.12310/) (2021)
- [Morphence: Moving Target Defense Against Adversarial Examples](https://mlsanity.com/paper/2108.13952/) (2021)
- [Explanation-Guided Diagnosis of Machine Learning Evasion Attacks](https://mlsanity.com/paper/2106.15820/) (2021)

## Abstract (arXiv)

An adversary who aims to steal a black-box model repeatedly queries the model via a prediction API to learn a function that approximates its decision boundary. Adversarial approximation is non-trivial because of the enormous combinations of model architectures, parameters, and features to explore. In this context, the adversary resorts to a best-effort strategy that yields the closest approximation. This paper explores best-effort adversarial approximation of a black-box malware classifier in the most challenging setting, where the adversary's knowledge is limited to a prediction label for a given input. Beginning with a limited input set for the black-box classifier, we leverage feature representation mapping and cross-domain transferability to approximate a black-box malware classifier by locally training a substitute. Our approach approximates the target model with different feature types for the target and the substitute model while also using non-overlapping data for training the target, training the substitute, and the comparison of the two. We evaluate the effectiveness of our approach against two black-box classifiers trained on Windows Portable Executables (PEs). Against a Convolutional Neural Network (CNN) trained on raw byte sequences of PEs, our approach achieves a 92% accurate substitute (trained on pixel representations of PEs), and nearly 90% prediction agreement between the target and the substitute model. Against a 97.8% accurate gradient boosted decision tree trained on static PE features, our 91% accurate substitute agrees with the black-box on 90% of predictions, suggesting the strength of our purely black-box approximation.