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Summary
This summary is machine-generated.

This study introduces an integrated cybersecurity testing evaluation method using anomaly detection and predictive analysis. The approach enhances test environment reliability and quantifies system security levels effectively.

Keywords:
autoencoder (AE)cybersecurity testingevaluation systemisolation forest (IF)

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Area of Science:

  • Cybersecurity
  • Computer Science
  • Systems Engineering

Background:

  • Cybersecurity testing is crucial for research, validation, and talent development.
  • A key challenge is creating reliable and reproducible testing environments efficiently.
  • Existing methods may lack robust evaluation metrics for system stability under attack.

Purpose of the Study:

  • To propose an integrated evaluation method for cybersecurity testing.
  • To address the need for efficient and reliable test environment construction.
  • To quantitatively assess the impact of cyberattacks on system stability.

Main Methods:

  • Utilized an autoencoder (AE) for dimensionality reduction of testbed data.
  • Applied the Isolation Forest (IF) algorithm for anomaly detection.
  • Quantified system disruption using Euclidean distance from a steady-state reference.

Main Results:

  • Demonstrated the feasibility and effectiveness of the proposed method via a case study.
  • Successfully quantified the degree of disruption caused by cyberattacks.
  • Introduced a percentage-based scoring mechanism for system security evaluation.

Conclusions:

  • The integrated method enhances cybersecurity testing by improving reliability and reproducibility.
  • The approach provides a quantitative measure of system security levels.
  • This method contributes to more robust validation of cybersecurity technologies and systems.