# Data Augmentation Based Malware Detection using Convolutional Neural Networks

arXiv: https://arxiv.org/abs/2010.01862 · HTML: https://mlsanity.com/paper/2010.01862/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Oct 2020.

## Built on by (most cited first, 8 of 8)

- [MalNet: A Large-Scale Image Database of Malicious Software](https://mlsanity.com/paper/2102.01072/) (2021)
- [Secure Multi-Party Computation based Privacy Preserving Data Analysis in Healthcare IoT Systems](https://mlsanity.com/paper/2109.14334/) (2021)
- [MalMixer: Few-Shot Malware Classification with Retrieval-Augmented Semi-Supervised Learning](https://mlsanity.com/paper/2409.13213/) (2024)
- [Data Augmentation for Opcode Sequence Based Malware Detection](https://mlsanity.com/paper/2106.11821/) (2021)
- [PBP: Post-training Backdoor Purification for Malware Classifiers](https://mlsanity.com/paper/2412.03441/) (2024)
- [Marvolo: Programmatic Data Augmentation for Practical ML-Driven Malware Detection](https://mlsanity.com/paper/2206.03265/) (2022)
- [On deceiving malware classification with section injection](https://mlsanity.com/paper/2208.06092/) (2022)
- [Malware Detection based on API calls](https://mlsanity.com/paper/2502.12863/) (2025)

## Abstract (arXiv)

Recently, cyber-attacks have been extensively seen due to the everlasting increase of malware in the cyber world. These attacks cause irreversible damage not only to end-users but also to corporate computer systems. Ransomware attacks such as WannaCry and Petya specifically targets to make critical infrastructures such as airports and rendered operational processes inoperable. Hence, it has attracted increasing attention in terms of volume, versatility, and intricacy. The most important feature of this type of malware is that they change shape as they propagate from one computer to another. Since standard signature-based detection software fails to identify this type of malware because they have different characteristics on each contaminated computer. This paper aims at providing an image augmentation enhanced deep convolutional neural network (CNN) models for the detection of malware families in a metamorphic malware environment. The main contributions of the paper's model structure consist of three components, including image generation from malware samples, image augmentation, and the last one is classifying the malware families by using a convolutional neural network model. In the first component, the collected malware samples are converted binary representation to 3-channel images using windowing technique. The second component of the system create the augmented version of the images, and the last component builds a classification model. In this study, five different deep convolutional neural network model for malware family detection is used.