# PUE Attack Detection by Using DNN and Entropy in Cooperative Mobile Cognitive Radio Networks
## 2 Previous Work
SUs in the CRN cooperate based on position estimation using the TDOA. Another author uses a proficient TDOA localization algorithm using the Differential Evolution (DE) method to detect the PUEA in CRNs, showing fast convergence of the detection [2]. Particle swarm optimization (PSO) has been used for PUE detection. The author in [14] uses a three-phase detection system: the state of the spectrum is calculated, the SVM algorithm is used to estimate if a frequency is occupied, and a PSO makes the selection of kernel and bias values. The technique is evaluated in MATLAB. In [15], the authors detect the PUE using the TDOA localization technique combined with a PSO algorithm to solve the cost function based on the TDOA results. It changes the parameters such as inertia, weight, and acceleration, but if a PUE, a SU, or a PU is in motion, these techniques will not work because they are based on the fixed position of the users. The AI has been used for the detection of PUE. In [16], the authors introduce a classification method called Online Adaptive Memory-Based Genetically Optimized Artificial Neural Network (OAM-GANN), based on online learning, that uses the network parameters to identify the PUE. It optimally tunes the hyperparameters of the developed DNN. It improves the security of the CRN. It is evaluated by several metrics, such as error rate and detection probability [16]. Other solutions are based on ML algorithms, such as the Knearest neighbor classifier (KNN) algorithm, which can identify the _PUE attack_ and group the malicious nodes together [17]. In another work, we compare KNN, random forest, and SVM, showing that for this problem, the best results are achieved with the SVM in an MCRN with a single SU [9]. Extreme machine learning (EML) and time-distance with signal strength evaluation (TDSE) methods have been used to detect or prevent PUE attacks. The TDSE implementation allows for identifying the malicious PUE attacker, and the EML compares it with the MU, but its computational requirements are high for a mobile network [18]. Another author uses the logistic regression concept to estimate the maximum likelihood of detecting the attack. The algorithm is divided into two parts: the training and testing processes, based on a dataset generated with an active _PUE attack_. Results show a high PD of about 99.5% with a PFA of less than 0.6% [5]. In another work, the authors implemented a classification problem and solved it with an artificial neural network (ANN). ANN increases its performance by using the immune plasma optimization (IPO) method. The results of this work show that the algorithm using the ANN has better results in terms of accuracy and other network variables. This method has an accuracy rate improvement of 32% and a 16% energy savings compared to the existing methods [19]. Other authors have proposed the DNN algorithm by combining energy detection, cyclostationary calculation, and the DNN algorithm. The PU/PUE in the frequency band is localized by using energy detection. Then, the cyclostationary technique identifies the features of the signal, and the results are used as input for the neural network to detect the _PUE attack_. Other work shows that radio-frequency fingerprinting can be used to detect the _PUE attack_. In the training step, the transmitter profiles are elaborated for each one, and a classifier with three layers of the ANN is created. The final part of the process is to compare the user signal to the established profiles to determine the PUE presence [5]. Most of the related previous work uses energy detection as the base for signal detection; we propose entropy detection for the _PUE attack_ because it is able to work at lower SNR values with a high probability of detection. The CSS has been worked on in simulations, but there have been no results with a realistic SDR environment as we implemented it in our work. Finally, in the literature, there are no implementations of the DNN algorithms to detect the _PUE attack_.