The threats posed by evolving cyberattacks have led to increased research related to software systems that can self-protect. One topic in this domain is Moving Target Defense (MTD), which changes software characteristics in the protected system to make it harder for attackers to exploit vulnerabilities. However, MTD implementation and deployment are often impacted by run-time uncertainties, and existing MTD decision-making solutions have neglected uncertainty in model parameters and lack self-adaptation. This paper aims to address this gap by proposing an approach for an uncertainty-aware and self-adaptive MTD decision engine based on Partially Observable Markov Decision Process and Bayesian Learning techniques. The proposed approach considers uncertainty in both state and model parameters; thus, it has the potential to better capture environmental variability and improve defense strategies. A preliminary study is presented to highlight the potential effectiveness and challenges of the proposed approach.

翻译：演化中的网络攻击带来的威胁促使与自保护软件系统相关的研究日益增多。该领域的一个研究主题是移动目标防御技术，该技术通过改变受保护系统的软件特征，增加攻击者利用漏洞的难度。然而，移动目标防御的实现与部署常受运行时不确定性的影响，现有移动目标防御决策方案忽视了模型参数中的不确定性，且缺乏自适应能力。本文旨在弥补这一不足，提出一种基于部分可观测马尔可夫决策过程和贝叶斯学习技术的不确定性感知自适应移动目标防御决策引擎。该方法同时考虑状态与模型参数中的不确定性，从而能更有效地捕捉环境变化并改进防御策略。本文通过初步研究展示了所提方法的潜在有效性与挑战。