# Adversarial Malware Generation in Linux ELF Binaries via Semantic-Preserving Transformations

arXiv: https://arxiv.org/abs/2604.22639 · HTML: https://mlsanity.com/paper/2604.22639/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 16 references that are in the map.
- First posted on arXiv 24 Apr 2026.

## Builds on (most cited first, 6 of 6)

- [Malware Detection by Eating a Whole EXE](https://mlsanity.com/paper/1710.09435/) (2017)
- [Learning to Evade Static PE Machine Learning Malware Models via Reinforcement Learning](https://mlsanity.com/paper/1801.08917/) (2018)
- [Malware Makeover: Breaking ML-based Static Analysis by Modifying Executable Bytes](https://mlsanity.com/paper/1912.09064/) (2019)
- [MERLIN -- Malware Evasion with Reinforcement LearnINg](https://mlsanity.com/paper/2203.12980/) (2022)
- [A Comparison of Adversarial Learning Techniques for Malware Detection](https://mlsanity.com/paper/2308.09958/) (2023)
- [Creating Valid Adversarial Examples of Malware](https://mlsanity.com/paper/2306.13587/) (2023)

## Abstract (arXiv)

Malware development and detection have undergone significant changes in recent years as modern concepts, such as machine learning, have been used for both adversarial attacks and defense. Despite intensive research on Windows Portable Executable (PE) files, there is minimal work on Linux Executable and Linkable Format (ELF). In this work, we summarize the academic papers submitted in this field and develop a new adversarial malware generator for the ELF format. Using a variety of metrics, we thoroughly evaluated our generator and achieved an Evasion Rate of 67.74 % while changing the confidence of the malware detector by -0.50 in the mean case for the dataset used. In our approach, we chose MalConv as the target classifier. Using this classifier, we found that the most successful modifications used strings typical of benign files as a data source. We conducted a variety of experiments and concluded that the target classifier appears sensitive to strings at any location within the executable file.