# Multiple PUE Attack Detection in Cooperative Mobile Cognitive Radio Networks
## 5 Results
### Energy Results  
For example, Figure 6 shows the energy calculation for the DL signal in 876.8 MHz, one of the frequencies in the selected range (876-880 MHz). The energy values are averaged, and if they are above the threshold, there is a signal of a PU/PUE, and the data are sent to the FC. This process is achieved for the DL and UL frequencies, and the results are sent to the DNN algorithm. In the learning process, the algorithm obtains the threshold. After this, in the detection process, when the system detects a suspicious signal, energy is measured in DL and UL and sent to the DNN algorithm. The SU achieves a site survey for authentication detection, which puts the operators' information in the coverage zone. THE MCC, MCN, and short name are saved and shared in the base station database. When the detection system starts, the SU tries to connect to the suspicious device and compare it with the database. The device is identified as a PU if the authentication process is successful. Nevertheless, it is a PUE if the information does not correspond to the database or the authentication process fails. After the authentication detection, the data are saved on the blacklist and shared with the FC. The parameters for the authentication process are shown in Table 2.  
The detection results are calculated and sent to the DNN system so that the final decision on each SU can be made. For the learning process, 1000 authentication samples were made in different grid positions. In the detection process, each SU makes one authentication process. The detection is valid for 923 samples, obtaining a PD of 92%. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Number of Samples & 10,000 samples \\ Averaged Values & 100 samples (10 \(\times\) 10) \\ Noise Signal & AWGN \\ Service & Phone Call-PUEA \\ Frequency & 876.8 MHz \\ Confidence Level & 95\% \\ Margin of Error & 5\% \\ Users & 3 SUs, 1 FC, 1/4 PUE \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters for energy detection learning. Figure 6: Example of energy detection (source: own). The USRP is connected to a PC with Ubuntu and OpenBTS. The system's base is GNURadio, and the algorithms are programmed in Python. The base system can be seen in Figure 7.  
For moving the SDR, we implement a system that moves just the antenna with the help of a quadcopter, which does not affect the users' received signals, as seen in Figure 8.  
For moving the SDR, we implement a system that moves only the antenna and not the SDR with the help of a quadcopter, which does not affect the users' received signals, as seen in Figure 8.  
and 10,000 samples were taken in the lowest part of the grid close to MCRN. With these samples, the confidence level is 95%, and the margin of error is 5%. This study delineates the hypothesis-testing framework for downlink and uplink signals. Downlink communication aims to distinguish the desired signal from noise by applying a defined threshold. Conversely, in uplink scenarios, the absence of a signal indi  
Figure 8: Mobile SDR device. Figure 9: Probability of detection vs. probability of false alarm results for AWGN channel (source: own). cates an unauthorized user. In contrast, the presence of a signal is attributed to a legitimate primary user, contingent upon prior validation of a downlink signal. Specifically, in the downlink context, a signal may be detected by either a legitimate primary user or a user attempting to mimic primary user behavior. In uplink communication, a detected signal unequivocally indicates a primary user, whereas its absence signifies the presence of an emulating user. Consequently, the overall probability of detecting a malicious user is determined by the product of the detection probabilities associated with both downlink and uplink transmissions. The Figure 10 shows the results of Montecarlo simulations in a CSS scheme for 1, 3, and 10 SUs with an SNR = \(-12\) dB expressed as ROC curves. As expected from the literature, the CSS has better results than individual detection.