Abstract
The development of new technologies in wireless domain provides better service to the users. The demand in wireless network services increases every day, and this leads to spectrum scarcity. Cognitive radio network is a solution for ideal spectrum sensing process. As the user and service increase, also the difficulties and security threats increase. The nature of a cognitive radio network provides better service with security, and this nature becomes vulnerable to the security threats. Resolving such vulnerabilities based on the analysis of CR network basic layers provides a secure network for better communication. This proposed research model is defined for obtaining secure CR network model using Neyman–Pearson criterion and an adaptive neuro-fuzzy inference system for detecting the attacks in the network. Experimental results highlight that proposed model is better in detection efficiency than artificial neural network-based detection models.
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Neelaveni, R., Sridevi, B. A novel Neyman–Pearson criterion-based adaptive neuro-fuzzy inference system (NPC-ANFIS) model for security threats detection in cognitive radio networks. Soft Comput 23, 8389–8397 (2019). https://doi.org/10.1007/s00500-019-04068-2
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DOI: https://doi.org/10.1007/s00500-019-04068-2