# HashTran-DNN: A Framework for Enhancing Robustness of Deep Neural Networks against Adversarial Malware Samples

arXiv: https://arxiv.org/abs/1809.06498 · HTML: https://mlsanity.com/paper/1809.06498/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2018.

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

- [PAD: Towards Principled Adversarial Malware Detection Against Evasion Attacks](https://mlsanity.com/paper/2302.11328/) (2023)
- [On the Security Risks of ML-based Malware Detection Systems: A Survey](https://mlsanity.com/paper/2505.10903/) (2025)
- [Adversarial Patterns: Building Robust Android Malware Classifiers](https://mlsanity.com/paper/2203.02121/) (2022)
- [Evaluating the robustness of adversarial defenses in malware detection systems](https://mlsanity.com/paper/2505.09342/) (2025)

## Abstract (arXiv)

Adversarial machine learning in the context of image processing and related applications has received a large amount of attention. However, adversarial machine learning, especially adversarial deep learning, in the context of malware detection has received much less attention despite its apparent importance. In this paper, we present a framework for enhancing the robustness of Deep Neural Networks (DNNs) against adversarial malware samples, dubbed Hashing Transformation Deep Neural Networks} (HashTran-DNN). The core idea is to use hash functions with a certain locality-preserving property to transform samples to enhance the robustness of DNNs in malware classification. The framework further uses a Denoising Auto-Encoder (DAE) regularizer to reconstruct the hash representations of samples, making the resulting DNN classifiers capable of attaining the locality information in the latent space. We experiment with two concrete instantiations of the HashTran-DNN framework to classify Android malware. Experimental results show that four known attacks can render standard DNNs useless in classifying Android malware, that known defenses can at most defend three of the four attacks, and that HashTran-DNN can effectively defend against all of the four attacks.