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This study introduces an improved image-based malware detection method using a novel two-branch deep network. The approach enhances malware classification performance by refining features and learning missing information.

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Artificial Intelligence

Background:

  • Malware classification is vital for cybersecurity, but current methods face performance limitations.
  • Image-based malware detection offers a promising alternative by converting binaries into visual data.

Purpose of the Study:

  • To propose a novel two-branch deep network for enhanced image-based malware classification.
  • To address limitations in existing malware detection techniques by refining feature extraction and incorporating auxiliary information.

Main Methods:

  • A two-branch deep network architecture was developed for malware image analysis.
  • The network incorporates faster asymmetric spatial attention for feature refinement.
  • An auxiliary feature branch was integrated to capture additional malware image information.

Main Results:

  • The proposed method demonstrated superior performance compared to state-of-the-art deep learning approaches.
  • Experimental results confirmed the effectiveness of the network across various evaluation metrics.
  • The integration of attention mechanisms and auxiliary branches significantly improved classification accuracy.

Conclusions:

  • The developed two-branch deep network offers a robust and effective solution for image-based malware classification.
  • This approach advances the field of malware detection by improving feature representation and learning.
  • The findings suggest a promising direction for future research in deep learning-based cybersecurity solutions.