Support Vector Machine-Based Classification of Malicious Users in Cognitive Radio Networks

Abstract EN

Cognitive radio is an intelligent radio network that has advancement over the traditional radio.

The difference between the traditional and cognitive radio is that all the unused frequency spectrum is utilized to the best of available resources in the cognitive setup unlike the traditional radio.

The main role of cognitive radio is spectrum sensing, in which the secondary users (SUs) opportunistically access the spectrum while avoiding interference to the primary user (PU) channel.

Various aspect of the spectrum sensing problem are studied from cognitive radio perspective.

Cooperative spectrum sensing in cognitive radio has a promising performance compared to the individual sensing.

However, the existence of the malicious users (MUs) highly degrades the performance of the cognitive radio network (CRN) by sending falsified results to the fusion center (FC).

In this paper, we proposed a machine learning algorithm called support vector machine (SVM) to classify normal SUs and MUs in the network.

SVM is used for both classification and regression, but mostly it is used for classification problems.

SVM clearly classify both normal and MUs by drawing hyper plane on the base of maximal margin.

The results of the legitimate SUs are combined at the FC by utilizing Dempster-Shafer (DS) evidence theory.

The effectiveness of the proposed scheme is demonstrated through simulation by comparing with the other existing schemes.