# Microsoft Malware Classification Challenge

arXiv: https://arxiv.org/abs/1802.10135 · HTML: https://mlsanity.com/paper/1802.10135/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Feb 2018.

## Built on by (most cited first, 30 of 46)

- [MOTIF: A Large Malware Reference Dataset with Ground Truth Family Labels](https://mlsanity.com/paper/2111.15031/) (2021)
- [A Framework for Cluster and Classifier Evaluation in the Absence of Reference Labels](https://mlsanity.com/paper/2109.11126/) (2021)
- [CNS-Net: Conservative Novelty Synthesizing Network for Malware Recognition in an Open-set Scenario](https://mlsanity.com/paper/2305.01236/) (2023)
- [Large Language Model (LLM) for Software Security: Code Analysis, Malware Analysis, Reverse Engineering](https://mlsanity.com/paper/2504.07137/) (2025)
- [MalNet: A Large-Scale Image Database of Malicious Software](https://mlsanity.com/paper/2102.01072/) (2021)
- [A Survey on Malware Detection with Graph Representation Learning](https://mlsanity.com/paper/2303.16004/) (2023)
- [Use of Graph Neural Networks in Aiding Defensive Cyber Operations](https://mlsanity.com/paper/2401.05680/) (2024)
- [Survey of Malware Analysis through Control Flow Graph using Machine Learning](https://mlsanity.com/paper/2305.08993/) (2023)
- [Assessing the Impact of Packing on Machine Learning-Based Malware Detection and Classification Systems](https://mlsanity.com/paper/2410.24017/) (2024)
- [A Comprehensive Study on Learning-Based PE Malware Family Classification Methods](https://mlsanity.com/paper/2110.15552/) (2021)
- [Semantic Preprocessing for LLM-based Malware Analysis](https://mlsanity.com/paper/2506.12113/) (2025)
- [Self-supervised Detransformation Autoencoder for Representation Learning in Open Set Recognition](https://mlsanity.com/paper/2105.13557/) (2021)
- [Representation learning with function call graph transformations for malware open set recognition](https://mlsanity.com/paper/2205.06918/) (2022)
- [Avast-CTU Public CAPE Dataset](https://mlsanity.com/paper/2209.03188/) (2022)
- [Malware Classification Using Transfer Learning](https://mlsanity.com/paper/2107.13743/) (2021)
- [Malware Classification Using Static Disassembly and Machine Learning](https://mlsanity.com/paper/2201.07649/) (2021)
- [Deep Image: A precious image based deep learning method for online malware detection in IoT Environment](https://mlsanity.com/paper/2204.01690/) (2022)
- [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)
- [Generative Adversarial Networks for Malware Detection: a Survey](https://mlsanity.com/paper/2302.08558/) (2023)
- [Defending Adversarial Patches via Joint Region Localizing and Inpainting](https://mlsanity.com/paper/2307.14242/) (2023)
- [Generalizing Machine Learning Evaluation through the Integration of Shannon Entropy and Rough Set Theory](https://mlsanity.com/paper/2404.12511/) (2024)
- [Machine Learning for Windows Malware Detection and Classification: Methods, Challenges and Ongoing Research](https://mlsanity.com/paper/2404.18541/) (2024)
- [A Progressive Transformer for Unifying Binary Code Embedding and Knowledge Transfer](https://mlsanity.com/paper/2412.11177/) (2024)
- [On the Security Risks of ML-based Malware Detection Systems: A Survey](https://mlsanity.com/paper/2505.10903/) (2025)
- [Adaptive Malware Detection using Sequential Feature Selection: A Dueling Double Deep Q-Network (D3QN) Framework for Intelligent Classification](https://mlsanity.com/paper/2507.04372/) (2025)
- [FedP3E: Privacy-Preserving Prototype Exchange for Non-IID IoT Malware Detection in Cross-Silo Federated Learning](https://mlsanity.com/paper/2507.07258/) (2025)
- [Signal-Based Malware Classification Using 1D CNNs](https://mlsanity.com/paper/2509.06548/) (2025)
- [A Non-Intrusive Machine Learning Solution for Malware Detection and Data Theft Classification in Smartphones](https://mlsanity.com/paper/2102.06511/) (2021)
- [A Novel Methodology For Crowdsourcing AI Models in an Enterprise](https://mlsanity.com/paper/2103.14033/) (2021)

## Abstract (arXiv)

The Microsoft Malware Classification Challenge was announced in 2015 along with a publication of a huge dataset of nearly 0.5 terabytes, consisting of disassembly and bytecode of more than 20K malware samples. Apart from serving in the Kaggle competition, the dataset has become a standard benchmark for research on modeling malware behaviour. To date, the dataset has been cited in more than 50 research papers. Here we provide a high-level comparison of the publications citing the dataset. The comparison simplifies finding potential research directions in this field and future performance evaluation of the dataset.