# MalNet: A Large-Scale Image Database of Malicious Software

arXiv: https://arxiv.org/abs/2102.01072 · HTML: https://mlsanity.com/paper/2102.01072/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 58 references that are in the map.
- First posted on arXiv 31 Jan 2021.

## Builds on (most cited first, 17 of 17)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Class-Balanced Loss Based on Effective Number of Samples](https://mlsanity.com/paper/1901.05555/) (2019)
- [Malware Detection by Eating a Whole EXE](https://mlsanity.com/paper/1710.09435/) (2017)
- [A Large-Scale Database for Graph Representation Learning](https://mlsanity.com/paper/2011.07682/) (2020)
- [Microsoft Malware Classification Challenge](https://mlsanity.com/paper/1802.10135/) (2018)
- [ELF: An Early-Exiting Framework for Long-Tailed Classification](https://mlsanity.com/paper/2006.11979/) (2020)
- [Imbalanced Malware Images Classification: a CNN based Approach](https://mlsanity.com/paper/1708.08042/) (2017)
- [Lightweight Classification of IoT Malware based on Image Recognition](https://mlsanity.com/paper/1802.03714/) (2018)
- [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)
- [REST: Robust and Efficient Neural Networks for Sleep Monitoring in the Wild](https://mlsanity.com/paper/2001.11363/) (2020)
- [AndroZoo++: Collecting Millions of Android Apps and Their Metadata for the Research Community](https://mlsanity.com/paper/1709.05281/) (2017)
- [D2M: Dynamic Defense and Modeling of Adversarial Movement in Networks](https://mlsanity.com/paper/2001.11108/) (2020)
- [New Era of Deeplearning-Based Malware Intrusion Detection: The Malware Detection and Prediction Based On Deep Learning](https://mlsanity.com/paper/1907.08356/) (2019)

## Built on by (most cited first, 11 of 11)

- [Position: Graph Foundation Models are Already Here](https://mlsanity.com/paper/2402.02216/) (2024)
- [AI-Driven Guided Response for Security Operation Centers with Microsoft Copilot for Security](https://mlsanity.com/paper/2407.09017/) (2024)
- [A Poincar'e Inequality and Consistency Results for Signal Sampling on Large Graphs](https://mlsanity.com/paper/2311.10610/) (2023)
- [Automated Malware Family Classification using Weighted Hierarchical Ensembles of Large Language Models](https://mlsanity.com/paper/2604.02490/) (2026)
- [Web Scale Graph Mining for Cyber Threat Intelligence](https://mlsanity.com/paper/2411.06239/) (2024)
- [MalVol-25: A Diverse, Labelled and Detailed Volatile Memory Dataset for Malware Detection and Response Testing and Validation](https://mlsanity.com/paper/2507.03993/) (2025)
- [Evaluating Out-of-Distribution Robustness in Graph-Based Android Malware Classification: A New Principled Benchmark](https://mlsanity.com/paper/2508.06734/) (2025)
- [Federated Cyber Defense: Privacy-Preserving Ransomware Detection Across Distributed Systems](https://mlsanity.com/paper/2511.01583/) (2025)
- [GoCoMA: Hyperbolic Multimodal Representation Fusion for Large Language Model-Generated Code Attribution](https://mlsanity.com/paper/2604.16377/) (2026)
- [TUANDROMD-X: Advanced Entropy and Visual Analytics Dataset for Enhanced Malware Detection and Classification](https://mlsanity.com/paper/2605.06718/) (2026)
- [MalTree: Tracing Malware Evolution from Embeddings at Scale](https://mlsanity.com/paper/2606.06570/) (2026)

## Abstract (arXiv)

Computer vision is playing an increasingly important role in automated malware detection with the rise of the image-based binary representation. These binary images are fast to generate, require no feature engineering, and are resilient to popular obfuscation methods. Significant research has been conducted in this area, however, it has been restricted to small-scale or private datasets that only a few industry labs and research teams have access to. This lack of availability hinders examination of existing work, development of new research, and dissemination of ideas. We release MalNet-Image, the largest public cybersecurity image database, offering 24x more images and 70x more classes than existing databases (available at https://mal-net.org). MalNet-Image contains over 1.2 million malware images -- across 47 types and 696 families -- democratizing image-based malware capabilities by enabling researchers and practitioners to evaluate techniques that were previously reported in propriety settings. We report the first million-scale malware detection results on binary images. MalNet-Image unlocks new and unique opportunities to advance the frontiers of machine learning, enabling new research directions into vision-based cyber defenses, multi-class imbalanced classification, and interpretable security.