Detection of Spectrum Sensing Data Falsification Attacker in CSS Cognitive Radio Networks

Cognitive Radio Networks (CRNs) are intelligent networks that adapt to changes in their environments to make the better use of unused spectrum. Cooperative spectrum sensing (CSS) seems to be more accurate and reliable by cognitive radios (CRs) as it takes advantage of cooperation among many CR devices. As a result of fading and shadowing effects, individual spectrum sensing (ISS) by CR is not precise and reliable. Hence, CSS is more preferable spectrum sensing scheme. However, CSS is more vulnerable to many security threats and attacks which often degrade the network performance. In this paper, we propose the protocol which identifies the spectrum sensing data falsification (SSDF) attacker and isolate it from networks. In SSDF attack, malicious users (MUs) sends false reports of spectrum sensing to fusion center (FC) with intention of influencing the final decision of FC about presence of primary user (PU) in spectrum band. As proposed protocol detects and eliminates the SSDF attacker, the network performance remains better although attacker occupy the network.