Intended inference lenient Secure Spectrum Sensing by prominence state verification

Abstract

Spectrum sensing is the process of identifying idle spectrums and utilizing them by the secondary users such that there is no inference with primary users. Cognitive Radio Network's (CRN) primary challenge is sensing of idle spectrum and efficiently handling that spectrum by the secondary user nodes. The effective and efficient spectrum sensing can achieve by enabling cooperation between nodes to share the information about spectrum state in cognitive radio networks. But there is a possibility of Spectrum Sensing Data Falsification (SSDF) attacks by malicious or selfish CRN nodes. This paper discusses the technique to assess the credibility of the neighbor nodes towards spectrum state verification process. The method referred as Intended Inference Lenient Secure Spectrum Sensing By prominence state verification (PSV) that devised in this paper aimed to prevent the intended data falsification by selfish or malicious neighbor nodes in cognitive radio networks. The simulations build on custom testbed with usual network conditions and SSDF attacks indicating that the devised model is greatly brought down the error rate of spectrum decision and at the same time improved the detection rate of malicious cognitive nodes.