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Related Experiment Videos

Vision transformer framework for host based cryptojacking malware detection.

Walid El-Shafai1,2,3, Ahmad Taher Azar4,5, Samah Alshathri6

  • 1College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia. welshafai@psu.edu.sa.

Scientific Reports
|July 6, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces CryptoIDS-ViT, a novel intrusion detection system (IDS) using vision transformers to effectively detect cryptojacking malware. The framework achieves high accuracy and efficiency, outperforming existing methods for enhanced cybersecurity.

Keywords:
Cryptojacking detectionDeep learning for cybersecurityHost-based malwareMalware image classificationReal-time intrusion detectionVision transformers

Related Experiment Videos

Area of Science:

  • Cybersecurity
  • Machine Learning
  • Computer Vision

Background:

  • Cryptojacking malware poses a growing threat, hijacking computing resources for unauthorized cryptocurrency mining.
  • Traditional intrusion detection systems (IDSs) struggle with cryptojacking due to accuracy limitations, high false positives, and poor adaptability.
  • Existing IDSs often fail to detect sophisticated cryptojacking attacks that evade conventional security measures.

Purpose of the Study:

  • To propose CryptoIDS-ViT, an advanced IDS framework utilizing vision transformer (ViT) architectures for robust host-based cryptojacking detection.
  • To introduce a novel, end-to-end detection framework adapting transformer models for cryptojacking mitigation in cybersecurity.
  • To address the need for accurate and efficient cryptojacking detection against emerging threats.

Main Methods:

  • Executable binaries are transformed into color and grayscale images for a novel image-based malware representation pipeline.
  • Multiple pre-trained transformer models (ViT, MaxViT, SwinViT) are custom-adapted and comparatively benchmarked for cryptojacking detection.
  • A two-stage process involving deep feature extraction and classification is employed for accurate detection and generalizability.

Main Results:

  • The SwinViT model achieved 99.35% accuracy (color) and 99.08% (grayscale), with high precision, recall, and F1-scores.
  • CryptoIDS-ViT demonstrated a 3-4% improvement over state-of-the-art CNN-based IDSs while maintaining real-time inference efficiency.
  • ViT attention heatmap visualizations provided enhanced interpretability and model transparency.

Conclusions:

  • CryptoIDS-ViT establishes ViT-based IDSs as a scalable, interpretable, and practical solution for detecting sophisticated cryptojacking malware.
  • The framework demonstrates strong generalizability across input types and deployment contexts, suitable for edge and enterprise systems.
  • This work offers a focused, high-impact contribution to host-based cryptojacking threat detection, an underexplored cybersecurity challenge.