# Multiple PUE Attack Detection in Cooperative Mobile Cognitive Radio Networks
## 5 Results
### DNN Results  
The DNN system takes the data from the two detection processes, energy, and authentication; some libraries, such as Sci-kit and Keras, were used. The results are obtained when a PUE and four simultaneous PUE attacks occur. The SNR is estimated with each measure as additional data for the results. With this experimental dataset, the DNN is calculated, and on each SU, it takes the final decision of the PUE attack detection and is sent to the FC for final decision. The DNN network architecture has the parameters defined in Table 3.  
SNR levels range from \(-25\) dB to \(0\) dB. The algorithm achieves a detection probability and accuracy of \(90\%\) at \(-9\) dB SNR. The confusion matrix of the trained algorithm uses 10,000 samples, as seen in Figure 14.  
We experiment with some epoch values to obtain precision and optimize the model, as seen in Figure 15.  
According to this experiment, \(50\) epochs are required to obtain good precision results, but with more, it shows higher precision results but involves more calculations. The number of neurons was initially \(16\), but we increased it to \(32\), keeping good precision results and balancing the computational complexity of the model. Using \(64\) neurons increases the precision results of the model by \(2\%\). The prediction probability increases using the DNN; individual energy and authentication models maintain an \(86\%\), while more than \(95\%\) are obtained with the DNN model. Figure 13: Available networks with PUE screen in the mobile phone (source: own). \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Hidden Layers & \(2\) \\ Neurons & \(32\) \\ Activation Function & ReLU and Softmax \\ Epochs & \(50\) \\ Bath Size & \(32\) \\ Total Parameters & \(642\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: DNN parameters for PUE detection. The DNN code is implemented with Keras in Python; part of the code can be seen in Figure 16.  
An OR logic mechanism estimates the PUE attack in the FC; if any SU identifies a PUE, an alert is broadcast from the FC to alert all SUs within the network. The probability of detecting a PUE attack is calculated as the number of correct PUE detections to the total instances in which a PUE is present. Experiments are carried out with one and four PUE attacks active on an SDR simultaneously at different work frequency ranges. The samples are taken from each dB in increments of \(-25\) dB to \(0\) dB, with the results illustrated in Figure 17. The receiver operation curves (ROC) for the probability of detection for a CSS energy detector based on [26] are compared with the DNN experimental results for a single and four PUE attacks. Figure 16: DNN code in Keras and Python (source: own). Figure 14: Confusion matrix -10 dB (source: author). Figure 15: DNN results depend on the epoch size (source: own). The results show that the SDR experiment achieves a probability of detection above 90% in -9 dB when there is a single PUE attack and \(-7\) dB with multiple PUE attacks. The PFA for DNN results is 5% for a single PUE attack and 10% for a multiple PUE attack. The results are compared with those of existing work with techniques like KNN and SVM [27] for \(-7\) dB of SNR. Table 4 shows the probability of detection for one or multiple PUE attacks and the measured false alarm rate. The SVM and KNN show the highest detection values, but they work only with a single PUE attack; other detectors for multiple PUs do not work correctly, but the DNN works with 90% of detection. The computational complexity is similar in the three cases; it is medium [28]. In the DNN model, the measured false positive rate is close to 10% (1042/10,000 samples), while the false negative is close to 5% (514/10,000 samples). The Table 5 describes the techniques' results individually and the DNN's. The individual energy detection shows a PD of 92% for one PUE and 87% for four PUE attacks. The authentication process obtains a PD of 90% with one PUE and 86% for multiple (four) PUE attacks.