Sparse Tent Chaotic <scp>PSO</scp> ‐Based <scp>SVM</scp> for Cooperative Spectrum Sensing Against Byzantine Attack in Cognitive Wireless Sensor Networks

ABSTRACT Cooperative spectrum sensing (CSS) in cognitive wireless sensor networks (CWSNs) is vulnerable to Byzantine attacks, resulting in reduced detection reliability. Although existing defense methods based on support vector machine (SVM) can enhance robustness, their parameter optimization relies on iterative cross‐validation (CV), which incurs significant computational overhead and limits their application in resource‐constrained environments. To address the efficiency bottleneck of SVM hyperparameter optimization in Byzantine‐robust CSS, this letter proposes a sparse tent chaotic particle swarm optimization SVM (STCPSO‐SVM) method. The novelty lies in integrating tent‐chaotic particle initialization with a stagnation‐aware sparse fitness update strategy, so that CV is invoked only for selected particles or during stagnation recovery rather than for the full swarm at every iteration. Simulation results demonstrate that, under composite attacks, compared with the optimal PSO‐SVM, this method maintains a high detection accuracy whilst reducing computation time by approximately 24.9% and 53% for 220 and 500 training samples, respectively.