# Multi-view Representation Learning from Malware to Defend Against Adversarial Variants

arXiv: https://arxiv.org/abs/2210.15429 · HTML: https://mlsanity.com/paper/2210.15429/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 30 references that are in the map.
- First posted on arXiv 25 Oct 2022.

## Builds on (most cited first, 15 of 15)

- [On Evaluating Adversarial Robustness](https://mlsanity.com/paper/1902.06705/) (2019)
- [Training Very Deep Networks](https://mlsanity.com/paper/1507.06228/) (2015)
- [Malware Detection by Eating a Whole EXE](https://mlsanity.com/paper/1710.09435/) (2017)
- [A Survey of Multi-View Representation Learning](https://mlsanity.com/paper/1610.01206/) (2016)
- [Learn To Pay Attention](https://mlsanity.com/paper/1804.02391/) (2018)
- [Learning to Evade Static PE Machine Learning Malware Models via Reinforcement Learning](https://mlsanity.com/paper/1801.08917/) (2018)
- [Functionality-preserving Black-box Optimization of Adversarial Windows Malware](https://mlsanity.com/paper/2003.13526/) (2020)
- [Exploring Adversarial Examples in Malware Detection](https://mlsanity.com/paper/1810.08280/) (2018)
- [Security Evaluation of Pattern Classifiers under Attack](https://mlsanity.com/paper/1709.00609/) (2017)
- [Non-Negative Networks Against Adversarial Attacks](https://mlsanity.com/paper/1806.06108/) (2018)
- [Binary Black-box Evasion Attacks Against Deep Learning-based Static Malware Detectors with Adversarial Byte-Level Language Model](https://mlsanity.com/paper/2012.07994/) (2020)
- [A Multi-view Context-aware Approach to Android Malware Detection and Malicious Code Localization](https://mlsanity.com/paper/1704.01759/) (2017)
- [Defense Methods Against Adversarial Examples for Recurrent Neural Networks](https://mlsanity.com/paper/1901.09963/) (2019)
- [Single-Shot Black-Box Adversarial Attacks Against Malware Detectors: A Causal Language Model Approach](https://mlsanity.com/paper/2112.01724/) (2021)
- [On Adversarial Robustness of Synthetic Code Generation](https://mlsanity.com/paper/2106.11629/) (2021)

## Abstract (arXiv)

Deep learning-based adversarial malware detectors have yielded promising results in detecting never-before-seen malware executables without relying on expensive dynamic behavior analysis and sandbox. Despite their abilities, these detectors have been shown to be vulnerable to adversarial malware variants - meticulously modified, functionality-preserving versions of original malware executables generated by machine learning. Due to the nature of these adversarial modifications, these adversarial methods often use a textitsingle view of malware executables (i.e., the binary/hexadecimal view) to generate adversarial malware variants. This provides an opportunity for the defenders (i.e., malware detectors) to detect the adversarial variants by utilizing more than one view of a malware file (e.g., source code view in addition to the binary view). The rationale behind this idea is that while the adversary focuses on the binary view, certain characteristics of the malware file in the source code view remain untouched which leads to the detection of the adversarial malware variants. To capitalize on this opportunity, we propose Adversarially Robust Multiview Malware Defense (ARMD), a novel multi-view learning framework to improve the robustness of DL-based malware detectors against adversarial variants. Our experiments on three renowned open-source deep learning-based malware detectors across six common malware categories show that ARMD is able to improve the adversarial robustness by up to seven times on these malware detectors.