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Attention-Based Automated Feature Extraction for Malware Analysis.

Sunoh Choi1, Jangseong Bae2, Changki Lee2

  • 1Department of Computer Engineering, Honam University, Gwangju 62399, Korea.

Sensors (Basel, Switzerland)
|May 24, 2020
PubMed
Summary

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This study introduces an AI method using attention mechanisms to identify key system calls for detecting malicious files. This approach improves detection accuracy over traditional AI models.

Area of Science:

  • Cybersecurity
  • Artificial Intelligence
  • Machine Learning

Background:

  • The daily creation of numerous malicious files exploits zero-day vulnerabilities.
  • Traditional pattern-based antivirus software struggles with the scale of new threats.
  • Current artificial intelligence (AI)-based detection methods lack explainability regarding *why* files are malicious.

Purpose of the Study:

  • To propose an AI-based malicious file feature extraction method using an attention mechanism.
  • To enhance the interpretability of AI-driven malicious file detection.
  • To improve the accuracy of malicious file detection.

Main Methods:

  • Developed a feature extraction technique leveraging the attention mechanism.
  • Applied the attention mechanism to identify critical application program interface (API) system calls indicative of maliciousness.
Keywords:
attentiondeep learningmalware analysis

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  • Evaluated the method against conventional AI detection models.

    • Main Results:

    • The attention mechanism successfully identified important API system calls for malicious file classification.
    • Achieved an approximate 12% accuracy improvement compared to a convolutional neural network (CNN) model.
    • Demonstrated a 5% accuracy increase over a skip-connected long short-term memory (LSTM) based detection model.

    Conclusions:

    • The proposed attention mechanism-based feature extraction method enhances malicious file detection accuracy.
    • This approach offers greater interpretability by highlighting crucial API system calls.
    • The findings suggest a promising direction for more effective and explainable AI-driven cybersecurity solutions.