mlsanityData to 7 Oct 2026

A Survey of Machine Learning Methods and Challenges for Windows Malware Classification

arXiv 2006.09271 ↗First posted 15 Jun 2020cs.CR cs.LG stat.APcited by 33

Not on the map. Why?

Abstract (arXiv)

Malware classification is a difficult problem, to which machine learning methods have been applied for decades. Yet progress has often been slow, in part due to a number of unique difficulties with the task that occur through all stages of the developing a machine learning system: data collection, labeling, feature creation and selection, model selection, and evaluation. In this survey we will review a number of the current methods and challenges related to malware classification, including data collection, feature extraction, and model construction, and evaluation. Our discussion will include thoughts on the constraints that must be considered for machine learning based solutions in this domain, and yet to be tackled problems for which machine learning could also provide a solution. This survey aims to be useful both to cybersecurity practitioners who wish to learn more about how machine learning can be applied to the malware problem, and to give data scientists the necessary background into the challenges in this uniquely complicated space.

Key facts. Cited by 33 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Jun 2020.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 7; 2022: 6; 2023: 5; 2024: 5; 2025: 5; 2026: 5.

33 papers.

Sort:

  1. 2021Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-Artcited by 18
  2. 2025EMBER2024 -- A Benchmark Dataset for Holistic Evaluation of Malware Classifierscited by 18
  3. 2021MOTIF: A Large Malware Reference Dataset with Ground Truth Family Labelscited by 17
  4. 2021A Framework for Cluster and Classifier Evaluation in the Absence of Reference Labelscited by 16
  5. 2022Out of Distribution Data Detection Using Dropout Bayesian Neural Networkscited by 15
  6. 2024Assemblage: Automatic Binary Dataset Construction for Machine Learningcited by 12
  7. 2022MERLIN -- Malware Evasion with Reinforcement LearnINgcited by 10
  8. 2021Adversarial Transfer Attacks With Unknown Data and Class Overlapcited by 8
  9. 2021Rank-1 Similarity Matrix Decomposition For Modeling Changes in Antivirus Consensus Through Timecited by 7
  10. 2022AI-based Malware and Ransomware Detection Modelscited by 7
  11. 2021Towards an Automated Pipeline for Detecting and Classifying Malware through Machine Learningcited by 6
  12. 2024Holographic Global Convolutional Networks for Long-Range Prediction Tasks in Malware Detectioncited by 6
21 more
  1. 2024Is Function Similarity Over-Engineered? Building a Benchmarkcited by 6
  2. 2023AVScan2Vec: Feature Learning on Antivirus Scan Data for Production-Scale Malware Corporacited by 5
  3. 2023Small Effect Sizes in Malware Detection? Make Harder Train/Test Splits!cited by 4
  4. 2021A Comprehensive Study on Learning-Based PE Malware Family Classification Methodscited by 3
  5. 2024MalMixer: Few-Shot Malware Classification with Retrieval-Augmented Semi-Supervised Learningcited by 3
  6. 2025Zipf-Gramming: Scaling Byte N-Grams Up to Production Sized Malware Corporacited by 2
  7. 2022Toward the Detection of Polyglot Filescited by 1
  8. 2022Marvolo: Programmatic Data Augmentation for Practical ML-Driven Malware Detectioncited by 1
  9. 2022Parallel Instance Filtering for Malware Detectioncited by 1
  10. 2023Towards an in-depth detection of malware using distributed QCNNcited by 1
  11. 2024Use of Multi-CNNs for Section Analysis in Static Malware Detectioncited by 1
  12. 2025Adaptive Malware Detection using Sequential Feature Selection: A Dueling Double Deep Q-Network (D3QN) Framework for Intelligent Classificationcited by 1
  13. 2026Trident: Improving Malware Detection with LLMs and Behavioral Featurescited by 1
  14. 2023MalwareDNA: Simultaneous Classification of Malware, Malware Families, and Novel Malwarecited by 0
  15. 2023On the Effectiveness of Adversarial Samples against Ensemble Learning-based Windows PE Malware Detectorscited by 0
  16. 2025Malware Classification Leveraging NLP & Machine Learning for Enhanced Accuracycited by 0
  17. 2025Certifiably robust malware detectors by designcited by 0
  18. 2026Machine Learning Transferability for Malware Detectioncited by 0
  19. 2026Identifying Security Platform Product Abuse with Machine Learningcited by 0
  20. 2026Fast And Accurate Text Content File Type Identificationcited by 0
  21. 2026An Introduction to Compression-Based Machine Learningcited by 0