Joint cooperative spectrum sensing and primary user emulation attack detection in cognitive radio networks using fuzzy conditional entropy maximization. Issue 5 (27th January 2019)

• Record Type: Journal Article
• Authors: Banerjee, Avik; Maity, Santi P.
• Is Part Of: Transactions on emerging telecommunications technologies. Volume 30:Issue 5(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-01-27
• Subjects: Telecommunication -- Periodicals; 384.05
• DOI: 10.1002/ett.3567
• Languages: English
• ISSNs: 2161-5748

Abstract

Abstract: Cooperative spectrum sensing (CSS) in cognitive radio system shows improved performance on spectrum sensing but, at the same time, faces a common threat called primary user emulation attack (PUEA) that impersonates the primary user (PU). This work explores joint PU and PUEA detection on CSS as a multiclass hypothesis using fuzzy conditional entropy maximization. To meet the goal, a multithreshold approach is suggested using a constrained differential evolution algorithm search to determine an optimal set of fuzzy function parameters, which maximizes the conditional entropy on the energy values. The proposed work also investigates the performance of CSS in presence of a group of malicious users (as PUEA), which generates signals and directly sends the same to the fusion center. Numerical results through simulation illustrate the efficacy of the proposed scheme in terms of both reliable PU detection in presence or absence of PUEA/malicious user and separate detection of falsifying PU and energy consumption in CSS. Simulation results show that the proposed scheme offers ∼17.54% and ∼39.39% higher detection probability of PU over the existing works in presence of PUEA. Abstract : The present work explores PU and PUEA detection based on joint cooperative spectrum sensing (CSS) in a cognitive radio network using fuzzy conditional entropy maximization. A multi‐threshold based approach is suggested using a constrained Differential Evolution algorithm search to determine an optimal set of fuzzy function parameters which maximize the conditional entropy on the energy values. Simulation results illustrate the efficacy of the proposed scheme in terms of both reliable PU detection in presence or absence of PUEA/malicious user, separate detection of falsifying PU and energy consumption in CSS.