Beta Distribution Function-Based Cooperative Spectrum Sensing Against Byzantine Attack in Cognitive Wireless Sensor Networks

In order to explore more spectrum resources to support sensors and its related applications, cognitive wireless sensor networks (CWSNs) have emerged, and cooperative spectrum sensing (CSS) is its most critical technology. However, CSS is susceptible to Byzantine attack due to its open nature, resulting in wastage of spectrum resources and interference with primary users (PUs). To suppress the negative impact of Byzantine attack, this letter proposes a beta distribution function-based CSS (
$\beta $
DF-CSS), which includes a weighted sequential process and a beta reputation model. Based on sequential probability ratio test (SPRT), we integrate the proposed beta reputation model into SPRT, while improving and reducing the positive and negative impacts of reliable and unreliable sensor nodes on global decision, respectively. The final simulation results show that compared with SPRT and weighted sequential probability ratio test, the proposed
$\beta $
DF-CSS has outstanding effects in terms of the error probability and average number of samples under various attack ratios and probabilities.