mlsanityData to 7 Oct 2026

Survey of Machine Learning Techniques for Malware Analysis

arXiv 1710.08189 ↗First posted 23 Oct 2017cs.CRcited by 31

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Abstract (arXiv)

Coping with malware is getting more and more challenging, given their relentless growth in complexity and volume. One of the most common approaches in literature is using machine learning techniques, to automatically learn models and patterns behind such complexity, and to develop technologies to keep pace with malware evolution. This survey aims at providing an overview on the way machine learning has been used so far in the context of malware analysis in Windows environments, i.e. for the analysis of Portable Executables. We systematize surveyed papers according to their objectives (i.e., the expected output), what information about malware they specifically use (i.e., the features), and what machine learning techniques they employ (i.e., what algorithm is used to process the input and produce the output). We also outline a number of issues and challenges, including those concerning the used datasets, and identify the main current topical trends and how to possibly advance them. In particular, we introduce the novel concept of malware analysis economics, regarding the study of existing trade-offs among key metrics, such as analysis accuracy and economical costs.

Key facts. Cited by 31 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Oct 2017.

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Citing papers per year: 2021: 7; 2022: 10; 2023: 7; 2024: 3; 2025: 4.

31 papers.

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  1. 2021The Threat of Offensive AI to Organizationscited by 37
  2. 2021A Survey on Machine Learning Techniques for Source Code Analysiscited by 23
  3. 2022The Role of Machine Learning in Cybersecuritycited by 21
  4. 2021Deep Learning for Android Malware Defenses: a Systematic Literature Reviewcited by 18
  5. 2021Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-Artcited by 18
  6. 2022On The Empirical Effectiveness of Unrealistic Adversarial Hardening Against Realistic Adversarial Attackscited by 15
  7. 2023SoK: Pragmatic Assessment of Machine Learning for Network Intrusion Detectioncited by 13
  8. 2022MERLIN -- Malware Evasion with Reinforcement LearnINgcited by 10
  9. 2022Quo Vadis: Hybrid Machine Learning Meta-Model based on Contextual and Behavioral Malware Representationscited by 9
  10. 2022AI-based Malware and Ransomware Detection Modelscited by 7
  11. 2024Combating Concept Drift with Explanatory Detection and Adaptation for Android Malware Classificationcited by 7
  12. 2021An Ensemble of Pre-trained Transformer Models For Imbalanced Multiclass Malware Classificationcited by 5
19 more
  1. 2023AVScan2Vec: Feature Learning on Antivirus Scan Data for Production-Scale Malware Corporacited by 5
  2. 2023Efficient Concept Drift Handling for Batch Android Malware Detection Modelscited by 5
  3. 2023Change Management using Generative Modeling on Digital Twinscited by 4
  4. 2021A Comprehensive Study on Learning-Based PE Malware Family Classification Methodscited by 3
  5. 2023Creating Valid Adversarial Examples of Malwarecited by 3
  6. 2024Deep Multi-Task Learning for Malware Image Classificationcited by 3
  7. 2024KiNETGAN: Enabling Distributed Network Intrusion Detection through Knowledge-Infused Synthetic Data Generationcited by 3
  8. 2023A Survey on Cross-Architectural IoT Malware Threat Huntingcited by 2
  9. 2025Evaluating the Potential of Quantum Machine Learning in Cybersecurity: A Case-Study on PCA-based Intrusion Detection Systemscited by 2
  10. 2025Zipf-Gramming: Scaling Byte N-Grams Up to Production Sized Malware Corporacited by 2
  11. 2022Parallel Instance Filtering for Malware Detectioncited by 1
  12. 2021Using Static and Dynamic Malware features to perform Malware Ascriptioncited by 0
  13. 2022Malware Analysis with Symbolic Execution and Graph Kernelcited by 0
  14. 2022On the impact of dataset size and class imbalance in evaluating machine-learning-based windows malware detection techniquescited by 0
  15. 2022Classification of Web Phishing Kits for early detection by platform providerscited by 0
  16. 2022Clustering based opcode graph generation for malware variant detectioncited by 0
  17. 2023Case Study-Based Approach of Quantum Machine Learning in Cybersecurity: Quantum Support Vector Machine for Malware Classification and Protectioncited by 0
  18. 2025Imbalanced malware classification: an approach based on dynamic classifier selectioncited by 0
  19. 2025LLM-Generated Samples for Android Malware Detectioncited by 0