Random Distributed Detection with an Application to Cognitive Radio Byzantine Attack

In this paper, a distributed detection model is introduced for m-ary hypotheses testing where the local sensors quantize their decisions to messages with alphabet size of D and the number of local sensors is random following a Poisson distribution. This model can be applied to a wide variety of distributed detection problems including homogenous and heterogeneous networks, robust detection under security attacks, and sensor failure mode analysis. As an illustrative example, the proposed model is applied to a Cognitive Radio network where the performance and strategies regarding Byzantine attacks are investigated under a game theoretical setting. Performance tradeoff between the detection efficiency and robustness of the sensor network is evaluated under the independent Byzantine attack model, where the malicious nodes attack based solely on their own observations. It is shown that, when the system is designed for maximum efficiency versus optimal robustness, then the malicious users may completely blind the fusion center, with less than one half of the total number of sensors.