mlsanityData to 7 Oct 2026

SOREL-20M: A Large Scale Benchmark Dataset for Malicious PE Detection

arXiv 2012.07634 ↗First posted 14 Dec 2020cs.CRcited by 49

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Abstract (arXiv)

In this paper we describe the SOREL-20M (Sophos/ReversingLabs-20 Million) dataset: a large-scale dataset consisting of nearly 20 million files with pre-extracted features and metadata, high-quality labels derived from multiple sources, information about vendor detections of the malware samples at the time of collection, and additional ``tags'' related to each malware sample to serve as additional targets. In addition to features and metadata, we also provide approximately 10 million ``disarmed'' malware samples -- samples with both the optionalheaders.subsystem and fileheader.machine flags set to zero -- that may be used for further exploration of features and detection strategies. We also provide Python code to interact with the data and features, as well as baseline neural network and gradient boosted decision tree models and their results, with full training and evaluation code, to serve as a starting point for further experimentation.

Key facts. Cited by 49 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Dec 2020.

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Citing papers per year: 2021: 5; 2022: 9; 2023: 5; 2024: 5; 2025: 15; 2026: 10.

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  5. 2025Large Language Model (LLM) for Software Security: Code Analysis, Malware Analysis, Reverse Engineeringcited by 15
  6. 2022Jigsaw Puzzle: Selective Backdoor Attack to Subvert Malware Classifierscited by 14
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  8. 2021Leveraging Uncertainty for Improved Static Malware Detection Under Extreme False Positive Constraintscited by 11
  9. 2022MERLIN -- Malware Evasion with Reinforcement LearnINgcited by 10
  10. 2021Mate! Are You Really Aware? An Explainability-Guided Testing Framework for Robustness of Malware Detectorscited by 7
  11. 2022Efficient Malware Analysis Using Metric Embeddingscited by 6
  12. 2024Is Function Similarity Over-Engineered? Building a Benchmarkcited by 6
37 more
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  3. 2023AVScan2Vec: Feature Learning on Antivirus Scan Data for Production-Scale Malware Corporacited by 5
  4. 2022Class-wise and reduced calibration methodscited by 4
  5. 2023Small Effect Sizes in Malware Detection? Make Harder Train/Test Splits!cited by 4
  6. 2024Stabilizing Linear Passive-Aggressive Online Learning with Weighted Reservoir Samplingcited by 4
  7. 2023The Power of MEME: Adversarial Malware Creation with Model-Based Reinforcement Learningcited by 3
  8. 2025Semantic Preprocessing for LLM-based Malware Analysiscited by 3
  9. 2022Avast-CTU Public CAPE Datasetcited by 2
  10. 2023A Survey on Cross-Architectural IoT Malware Threat Huntingcited by 2
  11. 2025Binary BPE: A Family of Cross-Platform Tokenizers for Binary Analysiscited by 2
  12. 2026Cybersecurity is the True Frontier for Generative AI Success or Failurecited by 2
  13. 2022Stealing and Evading Malware Classifiers and Antivirus at Low False Positive Conditionscited by 1
  14. 2022Marvolo: Programmatic Data Augmentation for Practical ML-Driven Malware Detectioncited by 1
  15. 2024Machine Learning for Windows Malware Detection and Classification: Methods, Challenges and Ongoing Researchcited by 1
  16. 2025On the Security Risks of ML-based Malware Detection Systems: A Surveycited by 1
  17. 2025Efficient Malware Detection with Optimized Learning on High-Dimensional Featurescited by 1
  18. 2025Adaptive Malware Detection using Sequential Feature Selection: A Dueling Double Deep Q-Network (D3QN) Framework for Intelligent Classificationcited by 1
  19. 2025Demystifying the Role of Rule-based Detection in AI Systems for Windows Malware Detectioncited by 1
  20. 2025Binary-30K: A Heterogeneous Dataset for Deep Learning in Binary Analysis and Malware Detectioncited by 1
  21. 2025Clustering Malware at Scale: A First Full-Benchmark Studycited by 1
  22. 2026Automated Malware Family Classification using Weighted Hierarchical Ensembles of Large Language Modelscited by 1
  23. 2026Trident: Improving Malware Detection with LLMs and Behavioral Featurescited by 1
  24. 2021A Comparison of State-of-the-Art Techniques for Generating Adversarial Malware Binariescited by 0
  25. 2022On the impact of dataset size and class imbalance in evaluating machine-learning-based windows malware detection techniquescited by 0
  26. 2023Enhancing Enterprise Network Security: Comparing Machine-Level and Process-Level Analysis for Dynamic Malware Detectioncited by 0
  27. 2024On the Robustness of Malware Detectors to Adversarial Samplescited by 0
  28. 2025Evaluating Out-of-Distribution Robustness in Graph-Based Android Malware Classification: A New Principled Benchmarkcited by 0
  29. 2025Certifiably robust malware detectors by designcited by 0
  30. 2025Efficient Adversarial Malware Defense via Trust-Based Raw Override and Confidence-Adaptive Bit-Depth Reductioncited by 0
  31. 2026FARM: Few-shot Adaptive Malware Family Classification under Concept Driftcited by 0
  32. 2026Machine Learning Transferability for Malware Detectioncited by 0
  33. 2026LCC-LLM: Leveraging Code-Centric Large Language Models for Malware Attributioncited by 0
  34. 2026Quantifiable Uncertainty: A Stochastic Consensus Multi-Agent RAG Framework for Robust Malware Detectioncited by 0
  35. 2026EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usabilitycited by 0
  36. 2026Simpler Methods Work Better for L1 Penalized Logistic Models and Large Datasetscited by 0
  37. 2026Temporal Generalization and Explanation Stability of Control Flow Graph Neural Networks for Malware Detectioncited by 0