Regressive Schnorr Signature Based Deep Belief for Secured Data Routing In Cognitive Radio Network
DOI:
https://doi.org/10.31838/NJAP/08.02.11Keywords:
Cognitive Radio Network, Secure Data Routing, Poisson Regressive Analysis, Schnorr Signature, Deep Belief NetworkAbstract
Wireless communication technology, called, Cognitive Radio Network (CRN) permits lesser users to approve networks lacking nosy with main users. Data routing is a confront in CRNs due to different features of CR mechanism, to name a few being, dynamic spectrum frequency bands in CRN, compromising network performance and presence of malicious nodes, therefore reducing the overall network performance. Therefore, secure and reliable routing becomes major issue for this type of network. In order to overcome this issue, a secure data routing method employing deep learning called Regressive Trust-aware Schnorr Signature based Deep Belief Network (RTSS-DBN) in CRN is proposed. It involves the four different layers namely, one input layer, two hidden layers and one output layer for secured data routing in CRN. Initially, number of cognitive radio nodes is considered as input in the input layer. In order to handle the security issues in CRN to enhance the data confidentiality rate, we planned two novel processes, such as authentication process and shifted spectrum sensing process in two hidden layers for secure data routing. In the first process (i.e. first hidden layer), we describe an authentication mechanism to sense the mean CR users or malicious users and removed them in the ultimate sensing decision. The CRN based on a secure linear sensing model using Poisson Regressive Analysis function. In the second process (i.e. second hidden layer), a novel sensing mechanism based on discrete logarithm function designed to examine a wideband spectrum with a high probability of detection rate employing Schnorr Signature Cryptographic is proposed. Finally with high detection results are sent to the output layer therefore ensuring secure data routing in CRN. The performance validation of RTSS-DBN method in CRN is discerned under distinct processes. The results of simulations described that the proposed RTSS-DBN method significantly addressed on data confidentiality rate, packet delivery ratio with reduced end-to-end delay and routing overhead, therefore ensuring robust security.
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