# PUE Attack Detection by Using DNN and Entropy in Cooperative Mobile Cognitive Radio Networks
## 2 Previous Work  
The _PUE attack_ is caused when the PUE attacker tries to mimic the honest PU signal and emulates it, corrupting the data transmitted to the FC. PUE attacks are classified as always on and smart. Always on does not know the status of the PU; it sends the PUEsignals in the PU frequency band. Smart PUEs know the PU signal status and attack the network depending on the PU's presence. The SUs report wrong decisions to the FC, leading to a bad global decision. The PUE uses a free frequency for its signal or causes interference, which is called selfish or malicious PUE, respectively [11]. For PUE detection, there have been some schemes; some of them are individual detection, and other works explore the CSS. In [7], we propose a method that uses the entropy calculation of the received signal for spectrum sensing; the results increase the performance for low SNR values, and it works in a simple SU. The basis of this detection is the spectrum sensing system. There are several problems, such as multipath fading, shadowing, and receiver uncertainty, and in an MCRN, there are also different modulation types, and it is difficult to distinguish between them. A possible solution for some of these problems is to share the information of all the users of the CRN; this means sharing the spectrum sensing data, the detected signal, frequency, and bands of use since these data increase detection performance, providing a solution that can be implemented for an MCRN [6]. We implemented this solution only for spectrum sensing and not for PUE detection [8]. The CSS model has been used to improve the probability of detection by including cooperation in the SUs, which share the particular sensing data and make a combined decision using an AND, OR, or MAJORITY rule, obtaining better results than the individual decisions of each CR and giving the MCRN a better knowledge of the radio environment. Energy-based detection is the faster and simpler method for spectrum sensing in the proposed CSS schemes. This method uses an energy threshold value to detect the presence of the PU/PUE in the radio environment, or with a test statistic and two thresholds, which obtain better results for sense. This is helpful to detect the signal, but it is not able to detect PUE attacks by itself [7]. In the CSS models, some authors have designed strategies to use the hard or soft fusion methods using MATLAB algorithms in additive white Gaussian noise (AWGN) or Rayleigh channels. The results show that the best method is a soft combination. Another platform used for implementing it on a device due to the costs and keeping high computational performance is the Raspberry Pi. Additionally, the SDR is another choice for CRN implementation [12]. The CSS model can be centralized using the FC; the cooperating SUs send their signal measurements or decisions about a sensing process to an FC or a base station BS. Then, the FC takes a general decision and transmits it to all the SUs. Another model uses a decentralized CSS, which is based on the sharing of data or decisions by the SUs to make a final decision. Still, it needs a radio frequency for the communication of each SU, requiring more radio resources. The authors use a centralized CSS to improve performance in deep fading environments with low SNR values. The PUE is a MU that reconfigures its air interfaces and transmits fake signals, leading the CSS system to make a wrong decision due to the SU report to the FC due to the openness of the lower layers [11]. Furthermore, apart from CSS, there are other techniques worth considering in the implementation of CRN. These techniques include the implementation of optimal joining techniques to detect PUEAs, detecting the MU, and bridging the gap between single equilibrium and social strategies through the imposition of a selected fee on SUs [13]. The time difference of arrival (TDOA) is another method used to find the PUE attacker position, but in an MCRN, this technique will not work because users are in motion.