论文标题
网络物理系统和保密中断概率：重新审视
Cyber-Physical-Systems and Secrecy Outage Probability: Revisited
论文作者
论文摘要
本文从技术上讲，在保密版本的凹入版本（SOP）的凹面版本中，本文探讨了$ \ Mathop {{{\ rm \ rm \ Mathbb {m} ax ax}}}} \ limits_ {δ{δ} {\ rm \ \ rm \ \; } \ Mathbb {p} \ Mathscr {r} \ big（λ\geλ\ big）$。即使我们使用传统的Wyner的窃听频道为我们的系统模型$ - $ - 可以扩展到各种保密的建模和分析，我们也从通用的角度来执行此操作。我们考虑了一个Riemannian mani折叠，并且数学上将其定义为$ \ Mathbb {V} \ Mathscr {ol} \ big \ big \lbraceλ\ big big \ rbrace $。通过实现Riemannian mani折叠及其体积的新结合，我们随后将其与相对概率闭合中存在的特征值数量相关联。我们借助一些有用的不平等现象，例如\ textit {Finsler's}柠檬，概括\ textit {Young's}不平等，广义\ textit {brunn-Minkowski} in fextit {Young's}不平等，证明了一些新颖的引理之间。我们还提出了一种基于马尔可夫决策过程的新颖新颖的强化学习算法，以找到与特征值分布有关的最佳策略$ - $ - $ $ $ $ $ $ $ $，这些东西扩展到周期性攻击案件的可能性半毫无疑问的决策过程。
This paper technically explores the secrecy rate $Λ$ and a maximisation problem over the concave version of the secrecy outage probability (SOP) as $\mathop{{\rm \mathbb{M}ax}}\limits_{Δ} {\rm \; } \mathbb{P}\mathscr{r} \big( Λ\ge λ\big) $. We do this from a generic viewpoint even though we use a traditional Wyner's wiretap channel for our system model $-$ something that can be extended to every kind of secrecy modeling and analysis. We consider a Riemannian mani-fold for it and we mathematically define a volume for it as $\mathbb{V}\mathscr{ol}\big \lbrace Λ\big \rbrace$. Through achieving a new bound for the Riemannian mani-fold and its volume, we subsequently relate it to the number of eigen-values existing in the relative probabilistic closure. We prove in-between some novel lemmas with the aid of some useful inequalities such as the \textit{Finsler's} lemma, the generalised \textit{Young's} inequality, the generalised \textit{Brunn-Minkowski} inequality, the \textit{Talagrand's} concentration inequality. We additionally propose a novel Markov decision process based reinforcement learning algorithm in order to find the optimal policy in relation to the eigenvalue distributions $-$ something that is extended to a possibilisitically semi-Markov decision process for the case of periodic attacks.
