# On deceiving malware classification with section injection

arXiv: https://arxiv.org/abs/2208.06092 · HTML: https://mlsanity.com/paper/2208.06092/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 53 references that are in the map.
- First posted on arXiv 12 Aug 2022.

## Builds on (most cited first, 24 of 24)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [The Effectiveness of Data Augmentation in Image Classification using Deep Learning](https://mlsanity.com/paper/1712.04621/) (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)
- [Deep Neural Network Based Malware Detection Using Two Dimensional Binary Program Features](https://mlsanity.com/paper/1508.03096/) (2015)
- [Microsoft Malware Classification Challenge](https://mlsanity.com/paper/1802.10135/) (2018)
- [Adversarial Perturbations Against Deep Neural Networks for Malware Classification](https://mlsanity.com/paper/1606.04435/) (2016)
- [Adversarial Deep Learning for Robust Detection of Binary Encoded Malware](https://mlsanity.com/paper/1801.02950/) (2018)
- [Detection of Word Adversarial Examples in Text Classification: Benchmark and Baseline via Robust Density Estimation](https://mlsanity.com/paper/2203.01677/) (2022)
- [Explaining Vulnerabilities of Deep Learning to Adversarial Malware Binaries](https://mlsanity.com/paper/1901.03583/) (2019)
- [Malware Makeover: Breaking ML-based Static Analysis by Modifying Executable Bytes](https://mlsanity.com/paper/1912.09064/) (2019)
- [Adversarial EXEmples: A Survey and Experimental Evaluation of Practical Attacks on Machine Learning for Windows Malware Detection](https://mlsanity.com/paper/2008.07125/) (2020)
- [Imbalanced Malware Images Classification: a CNN based Approach](https://mlsanity.com/paper/1708.08042/) (2017)
- [Activation Analysis of a Byte-Based Deep Neural Network for Malware Classification](https://mlsanity.com/paper/1903.04717/) (2019)
- [Lightweight Classification of IoT Malware based on Image Recognition](https://mlsanity.com/paper/1802.03714/) (2018)
- [Deep learning at the shallow end: Malware classification for non-domain experts](https://mlsanity.com/paper/1807.08265/) (2018)
- [COPYCAT: Practical Adversarial Attacks on Visualization-Based Malware Detection](https://mlsanity.com/paper/1909.09735/) (2019)
- [Data Augmentation Based Malware Detection using Convolutional Neural Networks](https://mlsanity.com/paper/2010.01862/) (2020)
- [Deep Transfer Learning for Static Malware Classification](https://mlsanity.com/paper/1812.07606/) (2018)
- [Towards Building an Intelligent Anti-Malware System: A Deep Learning Approach using Support Vector Machine (SVM) for Malware Classification](https://mlsanity.com/paper/1801.00318/) (2017)
- [Automatic Classification of Object Code Using Machine Learning](https://mlsanity.com/paper/1805.02146/) (2018)
- [Attacks on Visualization-Based Malware Detection: Balancing Effectiveness and Executability](https://mlsanity.com/paper/2109.10417/) (2021)

## Built on by (most cited first, 1 of 1)

- [EGAMA-RC: Risk-Calibrated Evidence-Gated Adaptive Malware Analysis for Robust and Interpretable Memory-Forensic Triage](https://mlsanity.com/paper/2608.22721/) (2026)

## Abstract (arXiv)

We investigate how to modify executable files to deceive malware classification systems. This work's main contribution is a methodology to inject bytes across a malware file randomly and use it both as an attack to decrease classification accuracy but also as a defensive method, augmenting the data available for training. It respects the operating system file format to make sure the malware will still execute after our injection and will not change its behavior. We reproduced five state-of-the-art malware classification approaches to evaluate our injection scheme: one based on GIST+KNN, three CNN variations and one Gated CNN. We performed our experiments on a public dataset with 9,339 malware samples from 25 different families. Our results show that a mere increase of 7% in the malware size causes an accuracy drop between 25% and 40% for malware family classification. They show that a automatic malware classification system may not be as trustworthy as initially reported in the literature. We also evaluate using modified malwares alongside the original ones to increase networks robustness against mentioned attacks. Results show that a combination of reordering malware sections and injecting random data can improve overall performance of the classification. Code available at https://github.com/adeilsonsilva/malware-injection.