Paper Info
Title
Support Vector Machine-Based Classification of Malicious Users in Cognitive Radio Networks
Abstract
Cognitive radio is an intelligent radio network that has advancement over traditional radio. The difference between the traditional radio and the cognitive radio is that all the unused frequency spectrum can be utilized to the best of available resources in the cognitive radio unlike the traditional radio. The core technology of cognitive radio is spectrum sensing, in which secondary users (SUs) opportunistically access the spectrum while avoiding interference to primary user (PU) channels. Various aspects of the spectrum sensing have been studied from the perspective of cognitive radio. Cooperative spectrum sensing (CSS) technique provides a promising performance, compared with individual sensing techniques. However, the existence of malicious users (MUs) highly degrades the performance of cognitive radio network (CRN) by sending falsified results to a fusion center (FC). In this paper, we propose a machine learning algorithm based on support vector machine (SVM) to classify legitimate SUs and MUs in the CRN. The proposed SVM-based algorithm is used for both classification and regression. It clearly classifies legitimate SUs and MUs by drawing a hyperplane on the base of maximal margin. After successful classification, the sensing results from the legitimate SUs are combined at the FC by utilizing Dempster-Shafer (DS) evidence theory. The effectiveness of the proposed SVM-based classification algorithm is demonstrated through simulations, compared with existing schemes. 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.
Year
DOI
Venue
2020
10.1155/2020/8846948
WIRELESS COMMUNICATIONS & MOBILE COMPUTING
DocType
Volume
ISSN
Journal
2020.0
1530-8669
Citations 
PageRank 
References 
0
0.34
0
Authors
6
Name
Order
Citations
PageRank
Muhammad Sajjad Khan132.76
Liaqat Khan200.34
Noor Gul375.90
Muhammad Amir400.34
Junsu Kim551.79
Su Min Kim612919.92