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A novel technique for ransomware detection using image based dynamic features and transfer learning to address

Jannatul Ferdous1, Rafiqul Islam2, Arash Mahboubi3

  • 1School of Computing, Mathematics and Engineering, Charles Sturt University, Wagga Wagga, NSW, 2650, Australia. jferdous@csu.edu.au.

Scientific Reports
|September 2, 2025
PubMed
Summary

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This study introduces a new ransomware detection method using behavior-to-image transformation and transfer learning (TL). This approach achieves high accuracy even with limited data, improving cybersecurity defenses against evolving ransomware threats.

Area of Science:

  • Cybersecurity
  • Machine Learning
  • Computer Forensics

Background:

  • Ransomware attacks are increasing, requiring advanced detection methods.
  • Current image-based detection often uses static analysis, missing dynamic behaviors.
  • Existing dynamic analysis methods lack spatial representation and struggle with scalability.

Purpose of the Study:

  • To develop a novel behavior-to-image ransomware detection framework.
  • To overcome limitations of manual feature engineering and large labeled datasets.
  • To enhance detection accuracy and generalization for ransomware threats.

Main Methods:

  • Transforming dynamic behavioral features into 2D grayscale and color images.
  • Utilizing transfer learning (TL) with pretrained models like ResNet50.
Keywords:
Convolutional neural networkDynamic analysisImage classificationPortable executable (PE)Pretrained modelsRansomwareTransfer learning

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  • Integrating domain-specific feature filtering and impact analysis.

    • Main Results:

    • Achieved up to 99.96% accuracy with a minimal loss factor of 0.0026.
    • Demonstrated effectiveness with a small dataset (500 ransomware, 500 benign samples).
    • Validated model interpretability using t-SNE visualizations and saliency maps.

    Conclusions:

    • The behavior-to-image framework with TL offers a scalable and adaptable solution for ransomware detection.
    • The approach effectively mitigates the need for extensive labeled data while maintaining high performance.
    • The model's transparency and accuracy suggest strong potential for practical cybersecurity deployment.