mlsanityData to 14 Oct 2026

Explainable Artificial Intelligence (XAI) for Malware Analysis: A Survey of Techniques, Applications, and Open Challenges

arXiv 2409.13723 ↗First posted 9 Sep 2024cs.CR cs.AIcited by 5

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Abstract (arXiv)

Machine learning (ML) has rapidly advanced in recent years, revolutionizing fields such as finance, medicine, and cybersecurity. In malware detection, ML-based approaches have demonstrated high accuracy; however, their lack of transparency poses a significant challenge. Traditional black-box models often fail to provide interpretable justifications for their predictions, limiting their adoption in security-critical environments where understanding the reasoning behind a detection is essential for threat mitigation and response. Explainable AI (XAI) addresses this gap by enhancing model interpretability while maintaining strong detection capabilities. This survey presents a comprehensive review of state-of-the-art ML techniques for malware analysis, with a specific focus on explainability methods. We examine existing XAI frameworks, their application in malware classification and detection, and the challenges associated with making malware detection models more interpretable. Additionally, we explore recent advancements and highlight open research challenges in the field of explainable malware analysis. By providing a structured overview of XAI-driven malware detection approaches, this survey serves as a valuable resource for researchers and practitioners seeking to bridge the gap between ML performance and explainability in cybersecurity.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 44 of its 133 references that are in the map. First posted on arXiv 9 Sep 2024.

Builds on

44 of 133 references.

References · 44

  1. 2017A Unified Approach to Interpreting Model Predictionscited by 6,555
  2. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
  3. 2014Going Deeper with Convolutionscited by 4,241
  4. 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,552
  5. 2017Learning Important Features Through Propagating Activation Differencescited by 1,458
  6. 2013Speech Recognition with Deep Recurrent Neural Networkscited by 669
  7. 2020Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Surveycited by 254
  8. 2020A Survey of the State of Explainable AI for Natural Language Processingcited by 206
  9. 2019Explainable Machine Learning for Scientific Insights and Discoveriescited by 166
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  11. 2017Malware Detection by Eating a Whole EXEcited by 115
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32 more
  1. 2020GAMI-Net: An Explainable Neural Network based on Generalized Additive Models with Structured Interactionscited by 82
  2. 2019Explainable AI for Trees: From Local Explanations to Global Understandingcited by 65
  3. 2017Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detectioncited by 52
  4. 2022Beyond Explaining: Opportunities and Challenges of XAI-Based Model Improvementcited by 48
  5. 2021Logic Explained Networkscited by 45
  6. 2019When Explainability Meets Adversarial Learning: Detecting Adversarial Examples using SHAP Signaturescited by 40
  7. 2021Explainable Artificial Intelligence Approaches: A Surveycited by 40
  8. 2015Text Understanding from Scratchcited by 25
  9. 2020An Explainable Machine Learning Model for Early Detection of Parkinson's Disease using LIME on DaTscan Imagerycited by 25
  10. 2020Can We Trust Your Explanations? Sanity Checks for Interpreters in Android Malware Analysiscited by 20
  11. 2019On Training Robust PDF Malware Classifierscited by 19
  12. 2022A Comparison of Static, Dynamic, and Hybrid Analysis for Malware Detectioncited by 17
  13. 2020Why an Android App is Classified as Malware? Towards Malware Classification Interpretationcited by 16
  14. 2016Iterative Orthogonal Feature Projection for Diagnosing Bias in Black-Box Modelscited by 12
  15. 2022Explainable AI for Android Malware Detection: Towards Understanding Why the Models Perform So Well?cited by 10
  16. 2021Malware Analysis with Artificial Intelligence and a Particular Attention on Results Interpretabilitycited by 9
  17. 2019I-MAD: Interpretable Malware Detector Using Galaxy Transformercited by 8
  18. 2020An explainable XGBoost-based approach towards assessing the risk of cardiovascular disease in patients with Type 2 Diabetes Mellituscited by 8
  19. 2018Deep Transfer Learning for Static Malware Classificationcited by 7
  20. 2021Mate! Are You Really Aware? An Explainability-Guided Testing Framework for Robustness of Malware Detectorscited by 7
  21. 2020Analyzing CNN Based Behavioural Malware Detection Techniques on Cloud IaaScited by 6
  22. 2018Explaining Black-box Android Malware Detectioncited by 4
  23. 2020Do Gradient-based Explanations Tell Anything About Adversarial Robustness to Android Malware?cited by 4
  24. 2019To believe or not to believe: Validating explanation fidelity for dynamic malware analysiscited by 2
  25. 2020Interpreting Machine Learning Malware Detectors Which Leverage N-gram Analysiscited by 2
  26. 2024Explainability Guided Adversarial Evasion Attacks on Malware Detectorscited by 2
  27. 2020DAEMON: Dataset-Agnostic Explainable Malware Classification Using Multi-Stage Feature Miningcited by 1
  28. 2021Towards Interpretable Ensemble Learning for Image-based Malware Detectioncited by 1
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  30. 2021Analyzing Machine Learning Approaches for Online Malware Detection in Cloudcited by 1
  31. 2021On the Effectiveness of Interpretable Feedforward Neural Networkcited by 1
  32. 2024Explainable Malware Detection with Tailored Logic Explained Networkscited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 5.

5 papers.

  1. 2026Small, Free, and Effective: Orchestrating Open-Weight Small Language Models to Outperform Single LLM for Malware Analysiscited by 1
  2. 2026Detecting Concept Drift in Evolving Malware Families Using Rule-Based Classifier Representationscited by 0
  3. 2026eDySec: A Deep Learning-based Explainable Dynamic Analysis Framework for Detecting Malicious Packages in PyPI Ecosystemcited by 0
  4. 2026CAM-Guided Saliency Cutout and Image-Based Malware Classificationcited by 0
  5. 2026EGAMA-RC: Risk-Calibrated Evidence-Gated Adaptive Malware Analysis for Robust and Interpretable Memory-Forensic Triagecited by 0