Analysis on Trustworthiness of Secondary Users using Machine Learning Approaches in Cognitive Radio Network Environment

The trustworthiness of Secondary Users (SUs) can be measured through its past and present trust values with sensing reputations given by the neighboring nodes in Cognitive Radio Network (CRN). Basing on these values, it is cleared that whether the SUs can be utilized the free channels of Primary Users (PUs) or not. In this paper, it has been proposed a model to analyze the trustworthiness of Secondary Users (SUs) in Cognitive Radio Network (CRN) with the help of Machine Learning (ML) approaches. It is desired to achieve more accuracy on the predicted data in the process of calculating trustworthiness and spectrum sensing reputation of SUs in Cognitive Radio Network (CRN). It has been also helped to sense the correct number of malicious users, suspicious users and honest users among the total number of SUs. For the simulation work WEKA software has been used, which is a collection of machine learning algorithms for data mining task. Three different types of classifiers of machine learning approaches have been analyzed in this simulation work such as Naive Bayes, Decision Tree and Bayes Network. From this analysis, it is observed that Decision Tree and Bayes Network are performing better than Navies Bayes in terms of providing high accuracy.