mlsanityData to 7 Oct 2026

Microsoft Malware Classification Challenge

arXiv 1802.10135 ↗First posted 22 Feb 2018cs.CRcited by 46

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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.

Key facts. Cited by 46 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Feb 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 9; 2022: 9; 2023: 12; 2024: 6; 2025: 7; 2026: 3.

46 papers.

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  1. 2021MOTIF: A Large Malware Reference Dataset with Ground Truth Family Labelscited by 17
  2. 2021A Framework for Cluster and Classifier Evaluation in the Absence of Reference Labelscited by 16
  3. 2023CNS-Net: Conservative Novelty Synthesizing Network for Malware Recognition in an Open-set Scenariocited by 15
  4. 2025Large Language Model (LLM) for Software Security: Code Analysis, Malware Analysis, Reverse Engineeringcited by 15
  5. 2021MalNet: A Large-Scale Image Database of Malicious Softwarecited by 11
  6. 2023A Survey on Malware Detection with Graph Representation Learningcited by 11
  7. 2024Use of Graph Neural Networks in Aiding Defensive Cyber Operationscited by 7
  8. 2023Survey of Malware Analysis through Control Flow Graph using Machine Learningcited by 5
  9. 2024Assessing the Impact of Packing on Machine Learning-Based Malware Detection and Classification Systemscited by 5
  10. 2021A Comprehensive Study on Learning-Based PE Malware Family Classification Methodscited by 3
  11. 2025Semantic Preprocessing for LLM-based Malware Analysiscited by 3
  12. 2021Self-supervised Detransformation Autoencoder for Representation Learning in Open Set Recognitioncited by 2
34 more
  1. 2022Representation learning with function call graph transformations for malware open set recognitioncited by 2
  2. 2022Avast-CTU Public CAPE Datasetcited by 2
  3. 2021Malware Classification Using Transfer Learningcited by 1
  4. 2021Malware Classification Using Static Disassembly and Machine Learningcited by 1
  5. 2022Deep Image: A precious image based deep learning method for online malware detection in IoT Environmentcited by 1
  6. 2022Marvolo: Programmatic Data Augmentation for Practical ML-Driven Malware Detectioncited by 1
  7. 2022On deceiving malware classification with section injectioncited by 1
  8. 2023Generative Adversarial Networks for Malware Detection: a Surveycited by 1
  9. 2023Defending Adversarial Patches via Joint Region Localizing and Inpaintingcited by 1
  10. 2024Generalizing Machine Learning Evaluation through the Integration of Shannon Entropy and Rough Set Theorycited by 1
  11. 2024Machine Learning for Windows Malware Detection and Classification: Methods, Challenges and Ongoing Researchcited by 1
  12. 2024A Progressive Transformer for Unifying Binary Code Embedding and Knowledge Transfercited by 1
  13. 2025On the Security Risks of ML-based Malware Detection Systems: A Surveycited by 1
  14. 2025Adaptive Malware Detection using Sequential Feature Selection: A Dueling Double Deep Q-Network (D3QN) Framework for Intelligent Classificationcited by 1
  15. 2025Signal-Based Malware Classification Using 1D CNNscited by 1
  16. 2021A Non-Intrusive Machine Learning Solution for Malware Detection and Data Theft Classification in Smartphonescited by 0
  17. 2021A Novel Methodology For Crowdsourcing AI Models in an Enterprisecited by 0
  18. 2022Sequence Feature Extraction for Malware Family Analysis via Graph Neural Networkcited by 0
  19. 2022Instance Attack:An Explanation-based Vulnerability Analysis Framework Against DNNs for Malware Detectioncited by 0
  20. 2022Feature Decoupling in Self-supervised Representation Learning for Open Set Recognitioncited by 0
  21. 2022Adversarial Attacks on Transformers-Based Malware Detectorscited by 0
  22. 2023Behavioural Reports of Multi-Stage Malwarecited by 0
  23. 2023Sequential Embedding-based Attentive (SEA) classifier for malware classificationcited by 0
  24. 2023ATWM: Defense against adversarial malware based on adversarial trainingcited by 0
  25. 2023Impact of Feature Encoding on Malware Classification Explainabilitycited by 0
  26. 2023Malware Classification using Deep Neural Networks: Performance Evaluation and Applications in Edge Devicescited by 0
  27. 2023Smart OMVI: Obfuscated Malware Variant Identification using a novel datasetcited by 0
  28. 2023High-resolution Image-based Malware Classification using Multiple Instance Learningcited by 0
  29. 2024kNN Classification of Malware Data Dependency Graph Featurescited by 0
  30. 2025FedP3E: Privacy-Preserving Prototype Exchange for Non-IID IoT Malware Detection in Cross-Silo Federated Learningcited by 0
  31. 2025Cross-Domain Malware Detection via Probability-Level Fusion of Lightweight Gradient Boosting Modelscited by 0
  32. 2026LCC-LLM: Leveraging Code-Centric Large Language Models for Malware Attributioncited by 0
  33. 2026A Hybrid Approach For Malware Classification Using Secondary Features Fusioncited by 0
  34. 2026Temporal Generalization and Explanation Stability of Control Flow Graph Neural Networks for Malware Detectioncited by 0