Particle bee optimized convolution neural network for managing security using cross-layer design in cognitive radio network

Abstract

Cognitive radio (CR) is one of the promising technologies for better utilization of radio spectrum. The parameters like operating frequency, transmission power, type of modulation etc., extend a major contribution in CR. These parameters helps to transmit the information in communication environment. The CR monitors their own performance and subsequently reads the radio outputs in radio frequency environment in an effective manner. During the process of this cognitive communication, security is one of the major issues owing to the factors such as anomalous spectrum usage, hindrance in the identification of primary and secondary spectrum users. This paper introduces Cross-Layer Design Based Particle Bee optimized Convolution Neural Network (CLPBCNN) for managing the security issues present in the cognitive radio networks. Initially, user’s spectrum has been sensed based on the energy level with cyclostationary process which helps to analyze the primary user related radio features. Along with the extracted user features, authentication is handled by symmetric triple data encryption algorithm and the features are trained by proposed network which examines the intermediate attacks and malicious activities. Furthermore, an efficient multi-path energy routing protocol is proposed to monitor the neighboring nodes and spectrum usage for detecting the attacks in CR. This process is repeated continuously for creating the effective attack free cognitive radio based wireless communication system. Then the excellence of the system is evaluated and validated with the help of NS2 simulation tool and the efficiency is analyzed in terms of miss detection probability, route dis-connectivity ratio, and detection delay. The effective utilization of this cyclostationary and routing process leads to overall improved performance.