One of the major trends in research on Self-Protecting Systems is to use a model of the system to be protected to predict its evolution. However, very often, devising the model requires special knowledge of mathematical frameworks, that prevents the adoption of this technique outside of the academic environment. Furthermore, some of the proposed approaches suffer from the curse of dimensionality, as their complexity is exponential in the size of the protected system. In this paper, we introduce a model-integrated approach for the design of Self-Protecting Systems, which automatically generates and solves Markov Decision Processes (MDPs) to obtain optimal defense strategies for systems under attack. MDPs are created in such a way that the size of the state space does not depend on the size of the system, but on the scope of the attack, which allows us to apply it to systems of arbitrary size.

中文翻译：

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一种设计自我保护系统的模型集成方法

自我保护系统研究的主要趋势之一是使用待保护系统的模型来预测其演变。然而，通常情况下，设计模型需要数学框架的特殊知识，这阻止了在学术环境之外采用这种技术。此外，一些提议的方法受到维数灾难的影响，因为它们的复杂性随着受保护系统的大小呈指数增长。在本文中，我们介绍了一种用于设计自我保护系统的模型集成方法，该方法自动生成并解决马尔可夫决策过程 (MDP)，以获得针对受攻击系统的最佳防御策略。MDP 的创建方式使得状态空间的大小不取决于系统的大小，而是取决于攻击的范围，