Robust Spectrum Sensing Scheme against Malicious Users Attack in a Cognitive Radio Network

Clement, J. Christopher; Sriharipriya, K. C.

doi:10.1109/icecet52533.2021.9698612

Cited by 3 publications

(1 citation statement)

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“…Ref. [ 9 ] surveys the attacks [ 10 ] and their detective measures in the physical layer of CRN. These attacks are mitigated using machine learning [ 11 ] and deep learning algorithms [ 12 , 13 ], as mentioned in [ 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

GRU-SVM Based Threat Detection in Cognitive Radio Network

Ezhilarasi

Clement

2023

Sensors

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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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