# Burning the Adversarial Bridges: Robust Windows Malware Detection Against Binary-level Mutations

arXiv: https://arxiv.org/abs/2310.03285 · HTML: https://mlsanity.com/paper/2310.03285/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 47 references that are in the map.
- First posted on arXiv 5 Oct 2023.

## Builds on (most cited first, 19 of 19)

- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [DeepFool: a simple and accurate method to fool deep neural networks](https://mlsanity.com/paper/1511.04599/) (2015)
- [Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods](https://mlsanity.com/paper/1705.07263/) (2017)
- [Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks](https://mlsanity.com/paper/1704.01155/) (2017)
- [On Detecting Adversarial Perturbations](https://mlsanity.com/paper/1702.04267/) (2017)
- [EMBER: An Open Dataset for Training Static PE Malware Machine Learning Models](https://mlsanity.com/paper/1804.04637/) (2018)
- [Malware Detection by Eating a Whole EXE](https://mlsanity.com/paper/1710.09435/) (2017)
- [Adversarial Examples Detection in Deep Networks with Convolutional Filter Statistics](https://mlsanity.com/paper/1612.07767/) (2016)
- [Adversarial Malware Binaries: Evading Deep Learning for Malware Detection in Executables](https://mlsanity.com/paper/1803.04173/) (2018)
- [Distributional Smoothing with Virtual Adversarial Training](https://mlsanity.com/paper/1507.00677/) (2015)
- [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)
- [Adversarial Deep Learning for Robust Detection of Binary Encoded Malware](https://mlsanity.com/paper/1801.02950/) (2018)
- [Dynamic Malware Analysis with Feature Engineering and Feature Learning](https://mlsanity.com/paper/1907.07352/) (2019)
- [Lightweight Classification of IoT Malware based on Image Recognition](https://mlsanity.com/paper/1802.03714/) (2018)
- [Adversary Resistant Deep Neural Networks with an Application to Malware Detection](https://mlsanity.com/paper/1610.01239/) (2016)
- [Learning the PE Header, Malware Detection with Minimal Domain Knowledge](https://mlsanity.com/paper/1709.01471/) (2017)
- [Detecting and Classifying Android Malware using Static Analysis along with Creator Information](https://mlsanity.com/paper/1903.01618/) (2019)
- [Malytics: A Malware Detection Scheme](https://mlsanity.com/paper/1803.03465/) (2018)

## Built on by (most cited first, 3 of 3)

- [Large Language Model (LLM) for Software Security: Code Analysis, Malware Analysis, Reverse Engineering](https://mlsanity.com/paper/2504.07137/) (2025)
- [Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection](https://mlsanity.com/paper/2507.22772/) (2025)
- [Adversarial Vulnerability Under Temporal Concept Drift: A Longitudinal Study of Android Malware Detection](https://mlsanity.com/paper/2605.23623/) (2026)

## Abstract (arXiv)

Toward robust malware detection, we explore the attack surface of existing malware detection systems. We conduct root-cause analyses of the practical binary-level black-box adversarial malware examples. Additionally, we uncover the sensitivity of volatile features within the detection engines and exhibit their exploitability. Highlighting volatile information channels within the software, we introduce three software pre-processing steps to eliminate the attack surface, namely, padding removal, software stripping, and inter-section information resetting. Further, to counter the emerging section injection attacks, we propose a graph-based section-dependent information extraction scheme for software representation. The proposed scheme leverages aggregated information within various sections in the software to enable robust malware detection and mitigate adversarial settings. Our experimental results show that traditional malware detection models are ineffective against adversarial threats. However, the attack surface can be largely reduced by eliminating the volatile information. Therefore, we propose simple-yet-effective methods to mitigate the impacts of binary manipulation attacks. Overall, our graph-based malware detection scheme can accurately detect malware with an area under the curve score of 88.32% and a score of 88.19% under a combination of binary manipulation attacks, exhibiting the efficiency of our proposed scheme.