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

## TL;DR

This paper introduces a self-evolving system that generates cyber attack scenarios to improve the evaluation and optimization of cybersecurity defenses.

## Contribution

The system introduces a real-time, scalable, and adaptable method for generating attack scenarios with self-evolving mechanisms.

## Key findings

- The system dynamically adapts attack scenarios based on specific goals and constraints.
- Simulation experiments validate the feasibility and adaptability of the generated attack scenarios.
- The system outperforms traditional tools like MulVAL in scalability and usability.

## Abstract

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.