Distributed estimation in the presence of attacks for large scale sensor networks
Distributed estimation in the presence of attacks for large scale sensor networks
复制标题
大规模传感器网络存在攻击时的分布式估计
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
Rick S. Blum
中科院分区：
文献类型：
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作者：
Jiangfan Zhang;Rick S. Blum
考虑在存在拜占庭攻击的情况下使用量化数据进行分布式估计。假设传感器的几个子集被不同的对手篡改。在对手的控制下，受损的传感器将虚构的数据传输到融合中心（FC），以破坏传感器网络的估计性能。首先，我们证明，只要已知未受攻击的传感器集合大于一次攻击所接管的任何受攻击传感器集合，就可以渐进地识别受攻击的传感器并将它们分类为对应于不同攻击的不同组。接下来，我们考虑攻击的统计描述和要估计的参数的联合估计。可以证明相应的Fisher信息矩阵(FIM)是奇异的。为了克服这个问题，提出了一种改进的量化方法，该方法将提供非奇异 FIM。因此，只要每个传感器的时间样本数量不小于2，就可以用足够的数据准确地估计攻击的统计特性和要估计的参数。此外，FIM用于提供必要和充分的条件，在该条件下，与忽略受损传感器的方法相比，以所提出的方式利用受损传感器将带来更好的估计性能。最后，数值结果表明，在某些情况下，可以通过利用受损的传感器来实现显着的估计性能增益。
Distributed estimation using quantized data in the presence of Byzantine attacks is considered. Several subsets of sensors are assumed to be tampered with by different adversaries. Under the control of adversaries, the compromised sensors transmit fictitious data to the fusion center (FC) in order to undermine the estimation performance of the sensor network. First, we show that it is possible to asymptotically identify the attacked sensors and categorize them into different groups corresponding to different attacks, provided it is known that the set of unattacked sensors is larger than any set of attacked sensors taken over by one attack. Next, we consider joint estimation of the statistical description of the attacks and the parameter to be estimated. It can be shown that the corresponding Fisher Information Matrix (FIM) is singular. To overcome this, a modified quantization approach is proposed, which will provide a nonsingular FIM. Thus, the statistical properties of the attacks and the parameter to be estimated can be accurately estimated with sufficient data, provided that the number of time samples at each sensor is not less than 2. Furthermore, the FIM is employed to provide necessary and sufficient conditions under which utilizing the compromised sensors in the proposed fashion will lead better estimation performance when compared to approaches where the compromised sensors are ignored. Finally, numerical results imply that for some cases, significant estimation performance gain can be achieved by taking advantage of compromised sensors.