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Evaluation of Malware Classification Models for Heterogeneous Data.

Ho Bae1

  • 1Department of Cyber Security, Ewha Womans University, Seoul 03760, Republic of Korea.

Sensors (Basel, Switzerland)
|January 11, 2024
PubMed
Summary
This summary is machine-generated.

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Machine learning (ML) models for malware classification are vulnerable to adversarial attacks. This study develops an explanation method, revealing that high accuracy can be misleading for these complex security systems.

Area of Science:

  • Cybersecurity
  • Machine Learning
  • Artificial Intelligence

Background:

  • Machine learning (ML) is widely used in technology security, including malware classification.
  • ML models are susceptible to adversarial examples, small input changes that alter predictions.
  • Adversarial attacks are particularly effective against malware classification models.

Purpose of the Study:

  • To explore the transparency of malware classification.
  • To develop an explanation method for malware classifiers.
  • To address the challenges of explaining heterogeneous malware data.

Main Methods:

  • Investigated the interpretability of malware classification models.
  • Developed a novel explanation method tailored for heterogeneous malware data.
Keywords:
IoTXAI for CTI applicationsXAI for cybersecurity dataadversarial learningdeep learninginterpretability

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  • Evaluated the effectiveness of current malware detectors and explanation techniques.

    • Main Results:

    • Existing explanation methods are inadequate for heterogeneous malware data.
    • Current malware detectors, despite high accuracy, can offer a false sense of security.
    • Classification accuracy alone is insufficient for validating the robustness of malware detectors.

    Conclusions:

    • Transparency and explainability are critical for robust malware classification.
    • New methods are needed to accurately assess the security provided by ML-based malware detectors.
    • Relying solely on accuracy metrics can be misleading in cybersecurity applications.