# A Convolutional Transformation Network for Malware Classification

arXiv: https://arxiv.org/abs/1909.07227 · HTML: https://mlsanity.com/paper/1909.07227/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2019.

## Built on by (most cited first, 6 of 6)

- [Malware Analysis with Artificial Intelligence and a Particular Attention on Results Interpretability](https://mlsanity.com/paper/2107.11100/) (2021)
- [Combining GCN and Transformer for Chinese Grammatical Error Detection](https://mlsanity.com/paper/2105.09085/) (2021)
- [Virus-MNIST: Machine Learning Baseline Calculations for Image Classification](https://mlsanity.com/paper/2111.02375/) (2021)
- [A Survey of Machine Learning Algorithms for Detecting Malware in IoT Firmware](https://mlsanity.com/paper/2111.02388/) (2021)
- [Improving Android Malware Detection Through Data Augmentation Using Wasserstein Generative Adversarial Networks](https://mlsanity.com/paper/2403.00890/) (2024)
- [A Visualized Malware Detection Framework with CNN and Conditional GAN](https://mlsanity.com/paper/2409.14439/) (2024)

## Abstract (arXiv)

Modern malware evolves various detection avoidance techniques to bypass the state-of-the-art detection methods. An emerging trend to deal with this issue is the combination of image transformation and machine learning techniques to classify and detect malware. However, existing works in this field only perform simple image transformation methods that limit the accuracy of the detection. In this paper, we introduce a novel approach to classify malware by using a deep network on images transformed from binary samples. In particular, we first develop a novel hybrid image transformation method to convert binaries into color images that convey the binary semantics. The images are trained by a deep convolutional neural network that later classifies the test inputs into benign or malicious categories. Through the extensive experiments, our proposed method surpasses all baselines and achieves 99.14% in terms of accuracy on the testing set.