## 5 Experimental Result  
As shown in Figure 12, the experimental data are acquired by universal software radio peripheral (USRP) with a sampling frequency of 48 MHz and a bandwidth of 10 MHz, and the type of IoT module is \(E\)05-_MLE_124_AP_2, which is a wireless transceiver module, operates at 2.4 GHz with small-size and 100 mW transmitting power. Because of its stable performance, small-size by professional hard design and being commonly used, this module are convenient for all kinds of embedded development. Therefore, we take this module representing the IoT devices to construct our experiment. Here, we collect the signals of the transmit module with the same receive module as the training samples, and the No.1 to No.3 IoT equipment are used as training classes, and No.5 to No.19 equipment are tested as unknown classes. Then, the test results are averaged after training. As shown in Figure 13, with the increasing of the test classes, the OCC rate decreases slowly, and the recognition rate of overall unknown class is above 90%. The OCC model not only needs to have an accurate identification of the unknown classes, but also the known classes. To this end, with No.1 to No.3 test samples inputting into this model, the recall rate of the known classes is 97.0% after average. It can be seen that the model can better identify the known classes. Figure 12: Experimental equipment. Figure 13: Result of the OCC-GAN model. OvR-SVM is one of the most common approaches to deal with the multi-class OCC problems based on the \"one-to-many\" training method, which is often used to construct multi-class identification. The improving OvR-SVM algorithm can get both class labels and probability values, while the OvR-SVM model detects the sample as an unknown class by setting a probability threshold. If the probability of the output is less than the threshold, the model will determine that the test sample is an unknown class. Otherwise, it is determined to be a known class. Figure 14 shows the average performance of the model for the test data. We can find that the recall rate of the model is high when the threshold is large, but the rate of the unknown classes is very low. While the threshold is small, the accuracy of the model increases rapidly, and the recall rate decreases rapidly. It can be seen from the horizontal axis that when the threshold is reduced from 0.600049 to 0.6, the performance of the model is seriously affected by the threshold. In order to compare the OvR-SVM with the OCC-GAN model, we set the threshold of this experiment as 0.600049 and train the models with the same 15 unknown test classes. The experimental results are shown in Figure 15. When the number of the test class is small, the unknown class recognition effect of the OCC-GAN model is slightly better. On the contrary, while the number of the test samples is increasing, the unknown class recognition effect of the OvR-SVM model is better. However, the recall rate of OvR-SVM is only 73%, which is far lower than the recall rate of 97% of the OCC-GAN model. Figure 14: Recognition rate of the OvR-SVM algorithm for OCC. Figure 15: Comparison of the OvR-SVM and OCC-GAN algorithms. In addition to the OvR-SVM algorithm, we also use the current OC-SVM algorithm for comparison. It is based on the Gaussian kernel nonlinear mapping. The features of each class are mapped to the same distribution space, and the spatial distance between the test class and the training class is used to determine whether it is an unknown category. The experimental comparison results are shown in Figure 16 and the overall performance of the OC-SVM is slightly lower than the OCC-GAN model. The recall rate of OCC-GAN is 97.0% as much as OC-SVM, and the accuracy of OC-SVM is only 88%. In comparison with the performance of the three algorithms above, the \(F1\) is defined to measure, as shown in Equation (20). We can get the result in Table 1, and the performance of the OCC-GAN model is the best.