mlsanityData to 13 Oct 2026

Malware Analysis with Artificial Intelligence and a Particular Attention on Results Interpretability

arXiv 2107.11100 ↗First posted 23 Jul 2021cs.CR cs.AIcited by 9

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Abstract (arXiv)

Malware detection and analysis are active research subjects in cybersecurity over the last years. Indeed, the development of obfuscation techniques, as packing, for example, requires special attention to detect recent variants of malware. The usual detection methods do not necessarily provide tools to interpret the results. Therefore, we propose a model based on the transformation of binary files into grayscale image, which achieves an accuracy rate of 88%. Furthermore, the proposed model can determine if a sample is packed or encrypted with a precision of 85%. It allows us to analyze results and act appropriately. Also, by applying attention mechanisms on detection models, we have the possibility to identify which part of the files looks suspicious. This kind of tool should be very useful for data analysts, it compensates for the lack of interpretability of the common detection models, and it can help to understand why some malicious files are undetected.

Key facts. Cited by 9 papers in the map (data through 13 Oct 2026). Builds on 8 of its 21 references that are in the map. First posted on arXiv 23 Jul 2021.

Builds on

8 of 21 references.

References · 8

  1. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,143
  2. 2017Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networkscited by 938
  3. 2018EMBER: An Open Dataset for Training Static PE Malware Machine Learning Modelscited by 165
  4. 2017Malware Detection by Eating a Whole EXEcited by 114
  5. 2018The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approachescited by 75
  6. 2018Non-Negative Networks Against Adversarial Attackscited by 20
  7. 2014Evolution and Detection of Polymorphic and Metamorphic Malwares: A Surveycited by 7
  8. 2019A Convolutional Transformation Network for Malware Classificationcited by 6

Built on by

Cited by year

Citing papers per year: 2021: 2; 2022: 2; 2023: 3; 2024: 2.

9 papers.

  1. 2022MERLIN -- Malware Evasion with Reinforcement LearnINgcited by 11
  2. 2022AI-based Malware and Ransomware Detection Modelscited by 7
  3. 2024Explainable Artificial Intelligence (XAI) for Malware Analysis: A Survey of Techniques, Applications, and Open Challengescited by 5
  4. 2023Quantum Machine Learning for Malware Classificationcited by 3
  5. 2021A Survey of Machine Learning Algorithms for Detecting Ransomware Encryption Activitycited by 1
  6. 2023Towards an in-depth detection of malware using distributed QCNNcited by 1
  7. 2024Explainable Malware Detection with Tailored Logic Explained Networkscited by 1
  8. 2021A Survey of Machine Learning Algorithms for Detecting Malware in IoT Firmwarecited by 0
  9. 2023MalwareDNA: Simultaneous Classification of Malware, Malware Families, and Novel Malwarecited by 0