论文标题
认知无线电网络中的对抗性鲁棒性
Adversarial Robustness in Cognitive Radio Networks
论文作者
论文摘要
\ textIt {当对手在对抗性鲁棒性模型中访问数据样本并可以做出数据依赖于数据的更改时，决策者如何深深依赖于对抗性化的数据，以进行统计推断？ 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 $ - $虽然拜占庭主要用户（PU）具有信噪比（SNR），其中包括$ \ frac {d \ ell \ big（θ| \ althsscr {s}} _0 \ big）} {d \ ell \ ell \ ell \ big（θ\ big）$ flue at in temulation to plemail的$ a $ a $ a $ a $ a的$ al \ eell \ eeld \ big（θ| \ mathscr {s}} _0 \ big）}上述弹性。我们的小说在线算法$ - $ $ $ $ $ \ mathbb {obrdht} $ $ - $ $ $和解决方案既独特又通用，在此过程中最终通过模拟$ - $ - 例如。除了错过检测概率与传感时间外，对总误差的评估是错误的警报概率。
\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.
