Less sample-Cooperative Spectrum Sensing Against Large-scale Byzantine
Attack in Cognitive Wireless Sensor Networks
Abstract
Cooperative spectrum sensing (CSS) has emerged as a promising
strategy for identifying available spectrum resources by leveraging
spatially distributed sensors in cognitive wireless sensor networks
(CWSNs). Nevertheless, this open collaborative approach is susceptible
to security threats posed by malicious sensors (MSs), specifically
Byzantine attack, which can significantly undermine CSS accuracy.
Moreover, in extensive CWSNs, the CSS process imposes substantial
communication overhead on the reporting channel, thereby considerably
diminishing cooperative efficiency. To tackle these challenges, this
article introduces a refined CSS approach, termed weighted sequential
detection (WSD). This method incorporates channel state information
(CSI) to validate the global decision made by the fusion center (FC) and
assess the trust value of sensors. The trust value based weight is
assigned to sensing samples, which are then integrated into a sequential
detection framework within a defined time window. This sequential
approach prioritizes samples based on descending trust values. Numerical
simulation results reveal that the proposed WSD outperforms conventional
fusion rules in terms of error probability and sample size, even under
varying degrees of Byzantine attack. This innovation signifies a
substantial advancement in enhancing the reliability and efficiency of
CSS.
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