An Attack Resilient Framework in Cognitive Radio Network Environment for Inter-domain and Intra-domain Communication

Rathee, Geetanjali; Jaglan, Naveen; Kanaujia, Binod Kumar

doi:10.1007/s11277-020-07541-6

Cited by 2 publications

(1 citation statement)

References 22 publications

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“…Blockchain is used to improve data security by performing secure spectrum sensing and beamforming [17], [18]. Hence, more attention is given to physical layer security, which prevents the network from attackers such as eavesdropping, jamming and primary user emulation (PUE), spectrum sensing data falsification (SSDF), and attacks such as Byzantine, to handle spectrum scarcity for increasing QoS [19], [20]. However, effective spectrum access and beamforming in a secure manner is still a demand of state-of-the-art.…”

Section: Introductionmentioning

confidence: 99%

BlockCRN-IoCV: Secure Spectrum Access and Beamforming for Defense Against Attacks in mmWave Massive MIMO CRN in 6G Internet of Connected Vehicles

Pari

Jaisankar

2022

IEEE Access

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Cognitive Radio (CR) is a wireless communication system that is used for intelligent vehicles to solve spectrum scarcity and improve the utilization of the spectrum. However, spectrum sensing and data sharing are difficult due to the presence of malicious nodes which degrades the performance. To overcome these issues, we proposed the BlockCRN-IoCV method which includes authentication, density aware clustering, dual agent based spectrum access and secure beamforming. Here, authentication is performed for both Primary Users (PUs) and Secondary Users (SUs) using the Hybrid Advanced Encryption Standard and Hyper-elliptic Curve Cryptography (AES-HCC) algorithm by considering ID, PUF and location which ensures the legitimacy of the users. To address the mobility of the vehicle we perform density aware clustering using Density aware Dynamic Radius Clustering (DADRC) by considering location, distance and direction for increasing throughput. After completing clustering, we perform efficient spectrum access by using the Dual Agent based Twin Delayed (DA-TD3) algorithm which includes two agents, the first agent performs spectrum sensing by considering SNR, noise level and trust, and the second agent performs spectrum allocation by considering Channel State Information (CSI), in which the CSI is predicted by Quasi-Newton Iterative Unscented Kalman Filter (QNIUKF) algorithm for effective data transmission. Finally, secure beamforming is performed using Bi-Gated Recurrent Neural Network (BiGRU-CapsNet) by considering CSI, beam score, array factor, and direction of angle. The simulation is carried out by OMNET++ and SUMO simulation tools and the performance of this work is evaluated by throughput, packet delivery ratio, SNR, detection accuracy, BER, and delay. The simulation result shows that the proposed work achieves superior performance compared to existing work for secure spectrum sensing and beamforming.

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