A self-evolution cyber attack scheme generation system for cybersecurity evaluation

With the increasing complexity of network attacks, defense systems face significant challenges in maintaining cybersecurity. To effectively evaluate and optimize defense strategies, this paper proposes a self-evolution attack scenario generation system tailored for assessment purposes. To address the scalability challenges in attack graph generation and improve the efficiency and relevance of security evaluations, the system incorporates a real-time generation method capable of dynamically adapting attack scenarios based on specific goals and constraints. Additionally, a methodology is developed to construct potential attack paths using attack graph techniques enhanced with self-evolving mechanisms. The feasibility and adaptability of the generated attack scenarios are validated through simulation experiments. This paper details the system's design, highlighting its core technical innovations-including incremental graph updates, scalable goal-driven path generation, and quantitative path ranking-which address key limitations of traditional tools like MulVAL. The system's effectiveness and superiority in scalability and usability are demonstrated through extensive simulations.