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False discovery rate based distributed detection in the presence of Byzantines

机译：拜占庭式存在下基于错误发现率的分布式检测

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摘要

Recent literature has shown that control of the false discovery rate (FDR) for distributed detection in wireless sensor networks (WSNs) can provide substantial improvement in detection performance over conventional design methodologies. In this paper, we further investigate system design issues in FDR-based distributed detection. We demonstrate that improved system design may be achieved by employing the Kolmogorov???Smirnov distance metric instead of the deflection coefficient, as originally proposed by Ray and Varshney in 2011.We also analyze the performance of FDR-based distributed detection in the presence of Byzantines. Byzantines are malicious sensors which send falsified information to the fusion center (FC) to deteriorate system performance. We provide analytical and simulation results on the global detection probability as a function of the fraction of Byzantines in the network. It is observed that the detection performance degrades considerably when the fraction of Byzantines is large. Hence, we propose an adaptive algorithm at the FC which learns the Byzantines??? behavior over time and changes the FDR parameter to overcome the loss in detection performance. Detailed simulation results are provided to demonstrate the robustness of the proposed adaptive algorithm to Byzantine attacks in WSNs.
机译：最近的文献表明，对于无线传感器网络（WSN）中的分布式检测，错误发现率（FDR）的控制可以提供比常规设计方法更大的检测性能。在本文中，我们将进一步研究基于FDR的分布式检测中的系统设计问题。我们证明，通过采用Kolmogorov ??? Smirnov距离度量代替偏转系数（如Ray和Varshney最初在2011年提出的那样），可以改进系统设计。我们还分析了基于FDR的分布式检测的性能。拜占庭人。拜占庭式传感器是恶意传感器，它将伪造的信息发送到融合中心（FC），从而降低系统性能。我们提供了关于全局检测概率与网络中拜占庭分数的函数的分析和模拟结果。观察到，当拜占庭的比例较大时，检测性能会大大降低。因此，我们在FC上提出了一种自适应算法，用于学习拜占庭语？？？随时间变化的行为，并更改FDR参数以克服检测性能的损失。提供了详细的仿真结果，以证明所提出的自适应算法对WSN中的拜占庭式攻击的鲁棒性。

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