Machine learning-based malicious user detection for reliable cooperative radio spectrum sensing in Cognitive Radio-Internet of Things

Machine Learning with Applications

Md Sipon Miah¹  Md Shamim Hossain² 

[1] Corresponding author.;Department of Information and Communication Technology, Islamic University, Kushtia 7003, Khulna, Bangladesh;

关键词: Cooperative spectrum sensing;    Cognitive radio;    Internet of Things;    Machine learning;    Support vector machine;    Cognitive Radio-Internet of Things;   

DOI  :  

来源: DOAJ

【 摘 要 】

The Cognitive Radio based Internet of Things (CR-IoT) is a promising technology that provides IoT endpoints, i.e., CR-IoT users the capability to share the radio spectrum otherwise allocated to licensed Primary Users (PUs). Cooperative Spectrum Sensing (CSS) improves spectrum sensing accuracy in a CR-IoT network. However, its performance may be degraded by potential attacks of the malicious CR-IoT users that send their incorrect sensing information to the corresponding Fusion Center (FC). This study presents a promising Machine Learning (ML)-based malicious user detection scheme for a CR-IoT network that uses a Support Vector Machine (SVM) algorithm to identify and classify malicious CR-IoT users. The classification allows the FC to make a more robust global decision based on the sensing results (i.e., energy vectors) which are reported only by the normal CR-IoT users. The effectiveness of the proposed SVM algorithm based ML in a CR-IoT network with the malicious CR-IoT users is verified via simulations.

【 授权许可】

Unknown