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Microsoft Malware Classification Challenge

Introduced in Microsoft Malware Classification Challenge22 Feb 2018 archive 2025-07-28

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.

Homepage Benchmarks Papers JSON

Benchmarks archive 2025-07-28

All 1 leaderboard whose dataset resolves to this page shown (sort by any header). "First row" is the archive's own first row at snapshot, in the archive's row order; nothing here re-ranks and metric direction is not asserted.

		First row (archive order)	Paper	Code
Malware Classification	Microsoft Malware Classification Challenge	Ahmadi et al. (2016): ENT, Bytes 1-G, STR, IMG1, IMG2, MD1, MISC, OPC, SEC, REG, DP, API, SYM, MD2 IMG and Opcode N-Grams + Ensemble Learning (XGBoost) Accuracy (10-fold) 0.9976	HYDRA: A multimodal deep learning framework for malware...	danielgibert/mlw_classification_hydra	29	Compare

Papers archive 2025-07-28

11 shown of 11 papers with a leaderboard row on this dataset's benchmarks, newest first. The archive's own "papers using this dataset" list was never published, so this is the benchmark-backed subset; the archive's count for this dataset is 36. The Syntology column is from Syntology's graph (read 2026-09-24), stated per sample; it is not part of any archive number.

			Date	Samples run Syntology
Sequential Embedding-based Attentive (SEA) classifier for malware classification	1	1	11 Feb 2023	not harvested
Designing Deep Convolutional Neural Networks using a Genetic Algorithm for Image-based Malware Classification	0	1	18 Jul 2022	not harvested
Malware Classification Using Static Disassembly and Machine Learning	2	1	10 Dec 2021	not harvested
Orthrus: A Bimodal Learning Architecture for Malware Classification	1	3	28 Sep 2020	not harvested
HYDRA: A multimodal deep learning framework for malware classification	1	8	12 May 2020	not harvested
A Hierarchical Convolutional Neural Network for Malware Classification	0	5	30 Sep 2019	not harvested
An End-to-End Deep Learning Architecture for Classification of Malware’s Binary Content	0	1	27 Sep 2018	not harvested
Using Convolutional Neural Networks for Classification of Malware represented as Images	1	3	27 Aug 2018	not harvested
Deep learning at the shallow end: Malware classification for non-domain experts	1	1	22 Jul 2018	not harvested
Classification of Malware by Using Structural Entropy on Convolutional Neural Networks	1	4	27 Apr 2018	not harvested
Convolutional Neural Network for Classification of Malware Assembly Code	1	1	27 Oct 2017	not harvested

Dataset loaders archive 2025-07-28

amsqr/Microsoft-Malware-BIG-2015-solution-0.0067-score-

sadiqkanner/malware-detection-

2 loaders as listed in the archive; links are outbound and not re-checked here.

Tasks archive 2025-07-28

Malware Classification

License archive 2025-07-28

No licence recorded in the archive. Absence here is not a statement about the dataset's terms.

Modalities archive 2025-07-28

No modality tagged.

Languages archive 2025-07-28

No language tagged.

Variants archive 2025-07-28

Microsoft Malware Classification Challenge

1 variant name, as the archive lists them.

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections