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arXiv 2201.01842cs.ITeess.SPmath.IT
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认知无线电网络中的对抗鲁棒性

Adversarial Robustness in Cognitive Radio Networks

Makan Zamanipour

摘要:

\textit{When an adversary gets access to the data sample in the adversarial robustness models and can make data-dependent changes, how has the decision maker consequently, relying deeply upon the adversarially-modified data, to make statistical inference? How can the resilience and elasticity of the network be literally justified $-$ if there exists a tool to measure the aforementioned elasticity?}针对认知无线电网络(CRN)$-$,研究了拜占庭弹性分布式假设检验(BRDHT)原理,可以扩展到任何类型的同构或异构网络$-$,而拜占庭主用户(PU)具有信噪比(SNR)包括与$α-$泄漏的时间速率相关的$\frac{d\ell \big ( θ| \mathscr{s}_0 \big )}{d\ell \big ( θ\big )} $系数作为测量上述弹性的适当工具。我们的新的在线算法$-$,这是命名为$\mathbb{OBRDHT}$ $-$和解决方案都是唯一的和通用的评估,最后通过模拟$-$,例如,总错误的评估,作为虚警概率,除了未命中检测概率与感测时间。

英文摘要:

\textit{When an adversary gets access to the data sample in the adversarial robustness models and can make data-dependent changes, how has the decision maker consequently, relying deeply upon the adversarially-modified data, to make statistical inference? How can the resilience and elasticity of the network be literally justified $-$ if there exists a tool to measure the aforementioned elasticity?} The principle of byzantine resilience distributed hypothesis testing (BRDHT) is considered in this paper for cognitive radio networks (CRNs) $-$ without-loss-of-generality, something that can be extended to any type of homogeneous or heterogeneous networks $-$ while the byzantine primary user (PU) has a signal-to-noise-ratio (SNR) including the coefficient of $\frac{d\ell \big ( θ| \mathscr{s}_0 \big )}{d\ell \big ( θ\big )} $ which is in relation to the temporal rate of the $α-$leakage as the appropriate tool to measure the aforementioned resilience. Our novel online algorithm $-$ which is named $\mathbb{OBRDHT}$ $-$ and solution are both unique and generic over which an evaluation is finally performed by simulations $-$ e.g. an evaluation of the total error as the false alarm probability in addition to the miss detection probability versus the sensing time.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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