Table 3 Deep learning reference work study
From: A survey on cognitive radio network attack mitigation using machine learning and blockchain
References | Type of attack | DL algorithm used |
|---|---|---|
[63] | PUEA | Neural network model |
[64] | PUEA | Secure hash algorithm and soft computing method (neural network) |
[65] | CUEA | ANN |
[66] | SSDF | Neural network classifier |
[67] | SSDF | Multilayer perceptron-based neural network |
[68] | Malicious user | ANN |
[69] | PUEA | Convolutional neural network |
[70] | PUEA and jamming attack | One-dimensional convolutional neural network |
[71] | PUEA, SSDF and eavesdropper attacks | Convolutional neural network (CNN), hybrid advance encryption with Diffie–Hellman encryption (HAES-DHE) algorithm |
[72] | Malicious user | RNN |
[73] | Malicious user | RNN |
[74] | PUEA | LSTM |
[75] | Spectrum anomaly | GAN |
[76] | PUEA | GAN |
[77] | PUEA | GAN |
[78] | Abnormal signals in CRN | Conditional generative adversarial network (C-GAN) and dynamic Bayesian network (DBN) |
[79] | Malicious user | GAN |
[80] | PUEA and jamming attack | Sparse coding method |
[81] | PUEA | Adaptive learning |
[82] | Random jamming attack | Autoencoder |
[83] | Jamming attack | Deep reinforcement learning |
[84] | Jamming attack | Deep reinforcement learning |