{"url":"/dataset/microsoft-malware-classification-challenge","name":"Microsoft Malware Classification Challenge","full_name":null,"description_markdown":"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.","description_withheld":null,"homepage":"https://www.kaggle.com/c/malware-classification/data","introduced_date":"2018-02-22","introduced_date_note":null,"introduced_by":{"paper":"/paper/microsoft-malware-classification-challenge","title":"Microsoft Malware Classification Challenge","first_author":null,"url":null},"license":null,"modalities":[],"tasks":[{"name":"Malware Classification","url":"/task/malware-classification","datasets_with_task":"/datasets/task/malware-classification"}],"languages":[],"variants":["Microsoft Malware Classification Challenge"],"data_loaders":[{"repo":"https://github.com/amsqr/Microsoft-Malware-BIG-2015-solution-0.0067-score-","url":"https://github.com/amsqr/Microsoft-Malware-BIG-2015-solution-0.0067-score-","frameworks":[]},{"repo":"https://github.com/sadiqkanner/malware-detection-","url":"https://github.com/sadiqkanner/malware-detection-","frameworks":[]}],"num_papers_in_archive":36,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/malware-classification-on-microsoft-malware","task":"Malware Classification","dataset_variant":"Microsoft Malware Classification Challenge","rows":29,"metrics":["Accuracy (10-fold)","LogLoss","Macro F1 (10-fold)","Accuracy (5-fold)","F1 score (5-fold)","Accuracy"],"first_row_in_archive_order":{"model":"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)","paper":"/paper/hydra-a-multimodal-deep-learning-framework","metrics":{"Accuracy (10-fold)":"0.9976","Macro F1 (10-fold)":"0.9931"},"code_links":[{"title":"danielgibert/mlw_classification_hydra","url":"https://github.com/danielgibert/mlw_classification_hydra"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/sequential-embedding-based-attentive-sea","title":"Sequential Embedding-based Attentive (SEA) classifier for malware classification","date":"2023-02-11","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/designing-deep-convolutional-neural-networks-1","title":"Designing Deep Convolutional Neural Networks using a Genetic Algorithm for Image-based Malware Classification","date":"2022-07-18","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/malware-classification-using-static","title":"Malware Classification Using Static Disassembly and Machine Learning","date":"2021-12-10","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/orthrus-a-bimodal-learning-architecture-for","title":"Orthrus: A Bimodal Learning Architecture for Malware Classification","date":"2020-09-28","rows_on_this_dataset":3,"code_links":1,"syntology":null},{"paper":"/paper/hydra-a-multimodal-deep-learning-framework","title":"HYDRA: A multimodal deep learning framework for malware classification","date":"2020-05-12","rows_on_this_dataset":8,"code_links":1,"syntology":null},{"paper":"/paper/a-hierarchical-convolutional-neural-network","title":"A Hierarchical Convolutional Neural Network for Malware Classification","date":"2019-09-30","rows_on_this_dataset":5,"code_links":0,"syntology":null},{"paper":"/paper/an-end-to-end-deep-learning-architecture-for-1","title":"An End-to-End Deep Learning Architecture for Classification of Malware’s Binary Content","date":"2018-09-27","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/using-convolutional-neural-networks-for-1","title":"Using Convolutional Neural Networks for Classification of Malware represented as Images","date":"2018-08-27","rows_on_this_dataset":3,"code_links":1,"syntology":null},{"paper":"/paper/deep-learning-at-the-shallow-end-malware","title":"Deep learning at the shallow end: Malware classification for non-domain experts","date":"2018-07-22","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/classification-of-malware-by-using-structural","title":"Classification of Malware by Using Structural Entropy on Convolutional Neural Networks","date":"2018-04-27","rows_on_this_dataset":4,"code_links":1,"syntology":null},{"paper":"/paper/convolutional-neural-network-for-1","title":"Convolutional Neural Network for Classification of Malware Assembly Code","date":"2017-10-27","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}