mlsanityData to 14 Oct 2026

ThreatVisionAI: A Hybrid CNN-ViT Framework for Image-Based Malware Classification

arXiv 2607.03653 ↗First posted 4 Jul 2026cs.CR cs.LGcited by 0

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Abstract (arXiv)

Traditional malware detection methods struggle to generalize to obfuscated or previously unseen threats. This paper introduces ThreatVisionAI, a hybrid malware family classification framework that integrates a raw-image CNN, a wavelet-based CNN, and a Vision Transformer (ViT) to capture complementary spatial, frequency-domain, and global relational features in malware images. The wavelet-based CNN captures multi-scale frequency information that helps distinguish closely related families, while the ViT branch models long-range dependencies across the image. Evaluated on the Malimg dataset, ThreatVisionAI achieves 98.01% accuracy and a weighted F1 score of 0.9742, with wavelet-domain features providing measurable gains on minority and visually similar families. These results confirm that frequency-aware and transformer-based representations improve image-based malware family classification.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 13 of its 35 references that are in the map. First posted on arXiv 4 Jul 2026.

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References · 13

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
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  4. 2023HuntGPT: Integrating Machine Learning-Based Anomaly Detection and Explainable AI with Large Language Models (LLMs)cited by 25
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  6. 2019Transfer Learning for Image-Based Malware Classificationcited by 12
  7. 2024A Survey of Malware Detection Using Deep Learningcited by 9
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  9. 2022Self-Supervised Vision Transformers for Malware Detectioncited by 6
  10. 2021Malware Detection Using Frequency Domain-Based Image Visualization and Deep Learningcited by 5
  11. 2021Stegomalware: A Systematic Survey of MalwareHiding and Detection in Images, Machine LearningModels and Research Challengescited by 4
  12. 2024Accelerating Malware Classification: A Vision Transformer Solutioncited by 2
1 more
  1. 2024Enhancing Cyber Security Through Predictive Analytics: Real-Time Threat Detection and Responsecited by 1

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