Enhanced Support Vector Machine for Cooperative Spectrum Sensing Against Byzantine Attack in Cognitive Wireless Sensor Networks

The spectrum scarcity and low utilization rates constrain further development possibilities in wireless sensor networks, the cognitive radio (CR) enables multiple sensor nodes (SNs) to detect whether the primary node (PN) is occupying spectrum bands by cooperative spectrum sensing (CSS) and provides an opportunity to access unused channels. To address the challenges posed by the inherent characteristics of wireless channels, CSS has emerged in a cognitive wireless sensor network (CWSN). However, this also provides opportunities for malicious SNs (MSNs) to manipulate spectrum sensing data by Byzantine attack. Therefore, in this article, we employ a support vector machine (SVM) to define the optimal hyperplane to distinguish MSNs from normal SNs (NSNs), and combine it with a novel algorithm associated with reputation values to exclude sensing results of MSNs, thereby improving the CSS performance of CWSNs. Furthermore, we also compare this enhanced SVM (ESVM) algorithm with other SVM approaches, evaluating the system’s detection probability, variations with changing parameters, and the stability of sensing performance. A series of simulation results demonstrate that ESVM exhibits better robustness against Byzantine attack, and the sensing accuracy is increased by 10.77%–14.21% compared with the existing method in the same scenario.