# PUE Attack Detection by Using DNN and Entropy in Cooperative Mobile Cognitive Radio Networks
## 1 Introduction  
In the era of Industry 4.0, applications, data management, and data analysis are needed. Technologies such as the Internet of Things (IoT), augmented or virtual reality, big data, and artificial intelligence need wireless technology that efficiently improves throughput, security, and spectrum access. Entities such as the Federal Communication Commission (FCC) recognize that part of the assigned radio-electric spectrum is not being used; its utilization can be under 15%. Cognitive radio networks (CRN) are a possibility for increasing the use of this spectrum by allowing a SU to use the spectrum if a PU is not using it, which requires continuous monitoring of the spectrum's use in a specific frequency range. If a PU starts to use a frequency, the SU must move to another frequency if there is another spectral hole in the assigned space. The spectral hole is a frequency channel not used at a specific time and can be used for the PU or SU. For example, if there are four free frequency channels to assign, a SU can use one of them, but if a PU wants to use it, the SU changes to any free channel of the three that remain or waits for one of them to be released to use it [1]. One possible solution to spectrum scarcity is the implementation of a CRN. In mobile networks, for example, there are time slots where the spectrum frequencies are unused, partly because their assignment is fixed by the regulatory entities in a traditional spectrum management system, giving the operators flexibility in their usage. However, if we were to implement the CRN, it would be crucial to acknowledge the accompanying securitythreats, including both traditional risks and those arising from the SU's random access to free channels for its own purposes [2]. The users in this environment are the PU and the SU, and then another user might appear, the MU, which uses radio signals to emulate the PU signal. The SU releases the occupied channel because they detect a PU, and the MU obtains access by cheating the SU. In the CRN environment, this process of searching the spectrum holes is called spectrum sensing and is one of the most affected processes in the network. However, all parts of the process are affected by this _PUE attack_. We must ensure that the SU uses empty spectrum bands without affecting the PU. To secure this, the CRN needs to check if the channel is occupied, coordinate with other users to access the spectrum, and release the channel if a PU starts transmitting its data [3]. One SU that detects the _PUE attack_ is affected by signal characteristics such as shadowing and multipath fading. Then, we can improve the detection system by including the CSS, which has a better probability of detecting the attack using several SU in a centralized or distributed architecture [4]. In CSS, each SU identifies the radio environment and looks for a PU signal; this local sensing information is sent to the FC, which centralizes all the SU information. The single detection of each one takes a global decision based on an established rule. For this scenario, the SU can send a simple bit with the decision about the PU presence or the averaged energy to the FC; this is called a hard or soft combination rule, respectively [1]. Recently, artificial intelligence (AI), which includes machine and deep learning, has been used for PUE detection; it shows an increase in detection performance compared with conventional schemes. It is close to the optimal strategy for classifying the attack data learned from many sample data sets. These algorithms can be applied in wireless communication systems for classifying communication, and they are more adaptive than conventional CSS schemes. Convolutional neural networks (CNNs) have been used for SU spectrum sensing, and CSS schemes based on machine learning (ML) models, such as the support vector machine (SVM), have been studied in [5]. The DNN has been used in PUE attacks to decrease the probability that a MU affects the CRN system.