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A Comparison of Static, Dynamic, and Hybrid Analysis for Malware Detection

arXiv 2203.09938 ↗First posted 13 Mar 2022cs.CR cs.LGcited by 17

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Abstract (arXiv)

In this research, we compare malware detection techniques based on static, dynamic, and hybrid analysis. Specifically, we train Hidden Markov Models (HMMs ) on both static and dynamic feature sets and compare the resulting detection rates over a substantial number of malware families. We also consider hybrid cases, where dynamic analysis is used in the training phase, with static techniques used in the detection phase, and vice versa. In our experiments, a fully dynamic approach generally yields the best detection rates. We discuss the implications of this research for malware detection based on hybrid techniques.

Key facts. Cited by 17 papers in the map (data through 14 Oct 2026). Builds on 0 of its 52 references that are in the map. First posted on arXiv 13 Mar 2022.

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Citing papers per year: 2022: 2; 2023: 6; 2024: 6; 2025: 1; 2026: 2.

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  1. 2023From Prompt Injections to SQL Injection Attacks: How Protected is Your LLM-Integrated Web Application?cited by 55
  2. 2024A Survey of Malware Detection Using Deep Learningcited by 9
  3. 2022Self-Supervised Vision Transformers for Malware Detectioncited by 6
  4. 2024Explainable Artificial Intelligence (XAI) for Malware Analysis: A Survey of Techniques, Applications, and Open Challengescited by 5
  5. 2023A Natural Language Processing Approach to Malware Classificationcited by 4
  6. 2023A Comparison of Graph Neural Networks for Malware Classificationcited by 3
  7. 2023Classification and Online Clustering of Zero-Day Malwarecited by 3
  8. 2023Creating Valid Adversarial Examples of Malwarecited by 3
  9. 2025Detecting Malicious Source Code in PyPI Packages with LLMs: Does RAG Come in Handy?cited by 3
  10. 2022Parallel Instance Filtering for Malware Detectioncited by 1
  11. 2023Benchmark of Data Preprocessing Methods for Imbalanced Classificationcited by 1
  12. 2024Leveraging Reinforcement Learning in Red Teaming for Advanced Ransomware Attack Simulationscited by 1
5 more
  1. 2024A Novel Approach to Malicious Code Detection Using CNN-BiLSTM and Feature Fusioncited by 1
  2. 2024Generative AI-Based Effective Malware Detection for Embedded Computing Systemscited by 0
  3. 2024Malware Classification using a Hybrid Hidden Markov Model-Convolutional Neural Networkcited by 0
  4. 2026EGAMA-RC: Risk-Calibrated Evidence-Gated Adaptive Malware Analysis for Robust and Interpretable Memory-Forensic Triagecited by 0
  5. 2026SkillAtlas: An Attack Trace Library for Agent Skillscited by 0