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This study introduces a self-evolving attack scenario generation system to enhance cybersecurity defense evaluations. It dynamically creates realistic attack paths, improving scalability and usability over traditional methods.

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Area of Science:

  • Cybersecurity
  • Network Defense
  • Artificial Intelligence

Background:

  • Increasing complexity of network attacks poses challenges to cybersecurity.
  • Existing defense evaluation methods lack scalability and efficiency.
  • Need for dynamic and adaptive security assessment tools.

Purpose of the Study:

  • Propose a self-evolution attack scenario generation system for cybersecurity assessment.
  • Enhance the efficiency, relevance, and scalability of security evaluations.
  • Develop a methodology for constructing self-evolving attack paths.

Main Methods:

  • Real-time, goal-driven attack scenario generation.
  • Attack graph techniques with self-evolving mechanisms.
  • Incremental graph updates and quantitative path ranking.

Main Results:

  • Validated feasibility and adaptability of generated attack scenarios via simulations.
  • Demonstrated superiority in scalability and usability compared to traditional tools (e.g., MulVAL).
  • Successfully addressed limitations of existing attack graph generation tools.

Conclusions:

  • The proposed system offers a scalable and adaptable solution for generating complex attack scenarios.
  • It significantly improves the evaluation and optimization of cybersecurity defense strategies.
  • The self-evolving mechanisms enhance the relevance and effectiveness of security assessments.