Cognitive Radio Network utilizes the spectrum resources intellectually. Spectrum sensing is the fundamental component of cognitive radio network. However, spectrum sensing is prone to many security attacks caused by malicious users. These attackers try to modify the sensed outcome to degrade the performance of the network. In our proposed model, inorder to identify and resist such malicious activities, Blockchain based technology is used in the fusion center for taking global decisions. The methodology of the proposed model consists of energy detection based Spectrum sensing and SHA-3 employed blockchain based malicious user detection. This detection process includes two phases : Block updation phase and iron out phase. The simulation results of the proposed method show 59% more detection probability at -18dB SNR compared to other conventional methods like equal gain combining (EGC) and Fault-tolerant cooperative spectrum sensing (FTCSS) and 35% more tracking probability when the number of malicious users is decreased. Thus the security of cognitive radio networks can be greatly improved using Blockchain technology.
The optical network plays a vital role in the current technology for providing high-speed data communication, which the networks operate at Tb/s. In this case, different modulation techniques can be used for different line rates to achieve high-speed data transmission. The light paths and various data rates such as 10, 40, and 100 Gb/s are the important parameters for mixed line rate networks. The wavelengths and line rates are powerful tools for mixed line rates networks. It can exist on different optical fibers. In this paper, advanced modulation techniques achieve a relative performance with the required Q-factor. This paper analysis for different matrix computations to achieve a superior Q-factor. It can affect the data rates and quality of transmission. This paper also proposed an algorithm that can improve the Q-factor. Q-factor is analyzed and proposes routing and wavelength assignment (RWA) techniques based on the Q-factor obtained at different line rates. This paper is a brief overview of a quality-aware path-finding algorithm for mixed line rates WDM/DWDM networks are present and discussed.
Cognitive radio networks are vulnerable to numerous threats during spectrum sensing. Different approaches can be used to lessen these attacks as the malicious users degrade the performance of the network. The cutting-edge technologies of machine learning and deep learning step into cognitive radio networks (CRN) to detect network problems. Several studies have been conducted utilising various deep learning and machine learning methods. However, only a small number of analyses have used gated recurrent units (GRU), and that too in software defined networks, but these are seldom used in CRN. In this paper, we used GRU in CRN to train and test the dataset of spectrum sensing results. One of the deep learning models with less complexity and more effectiveness for small datasets is GRU, the lightest variant of the LSTM. The support vector machine (SVM) classifier is employed in this study’s output layer to distinguish between authorised users and malicious users in cognitive radio network. The novelty of this paper is the application of combined models of GRU and SVM in cognitive radio networks. A high testing accuracy of 82.45%, training accuracy of 80.99% and detection probability of 1 is achieved at 65 epochs in this proposed work.
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