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. 2023 Jan 24;23(3):1326.
doi: 10.3390/s23031326.

GRU-SVM Based Threat Detection in Cognitive Radio Network

Evelyn Ezhilarasi I  1 , J Christopher Clement  1
Affiliations

GRU-SVM Based Threat Detection in Cognitive Radio Network

Evelyn Ezhilarasi I et al. Sensors (Basel). .

Abstract

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.

Keywords: cognitive radio network; gated recurrent unit; malicious users; spectrum sensing; support vector machine.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
GRU cell.
Figure 2
Figure 2
Update Gate.
Figure 3
Figure 3
Reset Gate.
Figure 4
Figure 4
Neural network representation of proposed GRU-SVM model.
Figure 5
Figure 5
Flowchart for classification of malicious and legitimate users using GRU-SVM model.
Figure 6
Figure 6
Evaluation metrics chart for training and testing data using GRU-SVM.
Figure 7
Figure 7
Training accuracy vs. Number of Epochs.
Figure 8
Figure 8
Testingaccuracy vs. Number of Epochs.
Figure 9
Figure 9
Probabilityof detection vs. Number of Epochs.
Figure 10
Figure 10
Probability of false alarm vs. Number of Epochs.
Figure 11
Figure 11
Confusion matrix-training.
Figure 12
Figure 12
Confusion matrix-validation.
See this image and copyright information in PMC

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