Trusted collaborative spectrum sensing for mobile cognitive radio networks

Abstract

Collaborative spectrum sensing is a key technology in cognitive radio networks (CRNs). It is inaccurate if spectrum sensing nodes are malicious. Although mobility is an inherent property of wireless networks, there has been no prior work studying the detection of malicious users for collaborative spectrum sensing in mobile CRNs. Existing solutions based on user trust for secure collaborative spectrum sensing cannot be applied to mobile scenarios, since they do not consider the location diversity of the network, thus over penalize honest users who are at locations with severe pathloss. In this paper, we propose to use two trust parameters, Location Reliability and Malicious Intention (LRMI), to improve malicious and primary user detection in mobile CRNs under attacks. Location Reliability reflects pathloss characteristics of the wireless channel and Malicious Intention captures the true intention of secondary users, respectively. Simulations of our proposed detection mechanisms, LRMI, show that mobility helps train location reliability and detect malicious users. We show an improvement of malicious user detection rate by 3 times and primary user detection rate by 20% at false alarm rate of 5%, respectively.