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MalFuzz: Coverage-guided fuzzing on deep learning-based malware classification model.

Yuying Liu1, Pin Yang1, Peng Jia1

  • 1School of Cyber Science and Engineering, Sichuan University, Chengdu, SiChuan, China.

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Summary
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MalFuzz enhances deep learning malware detection security by using coverage-guided fuzzing. This novel approach effectively tests deep learning models, improving cybersecurity against evolving malware threats.

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Artificial Intelligence

Background:

  • Deep learning models are increasingly used for malware detection due to limitations of traditional methods.
  • Securing these deep learning models is crucial as malware poses a significant cybersecurity threat.
  • Existing model testing methods primarily focus on image and natural language processing, neglecting deep learning-based malware detection.

Purpose of the Study:

  • To introduce MalFuzz, a novel framework for testing deep learning-based malware detection models.
  • To address the specific challenges in testing deep learning models for malware detection, including state representation and coverage calculation.
  • To develop tailored mutation and seed selection strategies for effective malware detection model testing.

Main Methods:

  • MalFuzz employs coverage-guided fuzzing principles adapted for deep learning malware detection.
  • It utilizes the first and last layer neuron values for approximate model state representation.
  • A fast approximate nearest neighbor algorithm is used for novel coverage computation.
  • Customized seed selection and mutation strategies are designed for malware detection models.

Main Results:

  • MalFuzz demonstrated superior effectiveness in detecting model classification errors compared to existing methods like TensorFuzz and MAB-malware.
  • The mutation strategy effectively preserves the original functionality of malware samples with high probability.
  • The seed selection strategy accelerates the exploration of the model state space, enhancing testing efficiency.

Conclusions:

  • MalFuzz provides a specialized and effective solution for testing the security of deep learning-based malware detection systems.
  • The framework successfully addresses key challenges in model state representation and coverage calculation for this domain.
  • MalFuzz offers a promising direction for improving the robustness and reliability of AI-driven cybersecurity solutions.