Secure cluster-based cooperative spectrum sensing against malicious attackers

The presence of malicious attackers in cognitive radio networks (CRNs) deeply degrades the overall performance in terms of detection accuracy, throughput and energy efficiency. A popular attack, called spectrum sensing data falsification (SSDF) attack, invades the CRN during cooperative spectrum sensing process. SSDF attack is represented by a malicious user that sends false sensing results to the fusion center, trying to mislead the global decision regarding the spectrum occupancy. Detecting such type of attack becomes a challenge especially in cluster-based CRNs. In this paper we propose an attacker-identification and removal algorithm that is able to detect attackers in cluster-based CRNs. The proposed algorithm requires that each transmitting user should send a report about the delivery of its transmitted data. The delivery report is then used to assess the local decisions of all users in order to recognize attackers and remove them. Mathematical formulation and computer simulation...