## 6 Experimental Results
### Result and Discussion  
In our work, the deep learning model framework utilized in this paper is based on TensorFlow 1.13. The equipment used during the training is configured as follows: GPU: NVIDIA GeForce GTX 1080 Ti and 2080 Ti. The CPU is Intel(R) Xeon(R) and CPU: E5-2678 v3 @ 2.50 GHz. In the first part, we compared the experimental results of different network depths in our designed deep residual network model. The purpose of this work was to select the optimal network model considering the available GPU space. In the network structure, we had five different types of residuals. In the 12 Layers model, their counts were [1, 1, 1, 1, 1]. In the 18 Layers model, their counts were [1, 1, 2, 2, 2]. In the 22 Layers model, their counts were [2, 2, 2, 2, 2]. In the 32 Layers model, their counts were [3, 3, 3, 3, 3]. The training experiment results on a randomly selected dataset showed that the 22 Layers model performed the best with an accuracy of 81.25% as shown in Figure 7, outperforming the other models. Therefore, we chose this network model for further experimental steps. \begin{table}
\begin{tabular}{l l l l} \hline \hline \multirow{3}{*}{**Dataset**} & \multicolumn{3}{c}{**DataSample**} \\ \cline{2-4}  & **Key** & **Plaintext** & **Ciphertext** \\ \cline{2-4}  & 0x3F1C77C5A86E5 AF & 0 xB9D1C48E348 FE77 & 0x42F7 EA0 BC6 AC69F \\  & 119A4073F51 FDAEA7 & 1 FA464A77A178 FB07 & DC282C7A447 BE3E55 \\  & 0x489 DB4B3F317296 & 0 xDCFeAD50D1D9 FD0 & 0 xE82201 EF806 DFA2 \\  & 1 CC2 BCB4 ED2E28 EB7 & 8B386 EFB08B142F74 & 28309 ED5B271805F2 \\  & 0 xB303 FDCEDB10838 & 0x4 CFDA4C3076157F & 0x30F9F5D7 DC56A51 \\  & 645 FEB0626 FACAD79 & 581B71E46E8 CB5675 & E7D8E030B45810 EA2 \\  & 0x3E5A862B58B1652 & 0x253868B79E7C31E & 0x5E96B2A5B349 FC2 \\  & 7F315F97 CA30B1E36 & 0D3 BCDBAE9F37E5E5 & 55B65D4356 CB19D1E \\  & 0x164F8C4 EFFE0 CB3 & 0x16D62 BA4D338 FF7 & 0 xA5B7 EBE2E8A9 FAD \\  & 8192D6 DF466C6F21F & BA6B8 CF19 BAB720E7 & 123476 AB59F0A99E2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Examples of encryption details. Figure 6: The signal of raw data. Schemes follow the same formatting. In the second part, we compared the impact of model parameter sharing on the accuracy of electromagnetic signal classification in transfer learning. Using the experimental platform described in Figure 5, we fixed the plaintext and key in the encryption algorithm and obtained a batch of electromagnetic signal data when all the information of the encryption algorithm was known. We considered this signal as ideal attack data. After training the model, we obtained an ideal classification template. Then, based on this model, we performed parameter-sharing training for the actual model we wanted to obtain. The experimental results showed that the network could achieve 100% classification accuracy in the ideal case. In the comparison of transfer learning methods, for the same training batch size, the network model that underwent early transfer showed higher accuracy compared to the model with weight initialization. The network model without transfer achieved an accuracy of 80.98%, while the network model after transfer reached an accuracy of 87.5%, resulting in an improvement of nearly 7% in accuracy, as shown in Figure 8.  
Figure 8: The influence of transfer learning. Figure 7: The comparison results of different network layers. In the third part, we validated the use of transfer learning methods for better performance in small-sample datasets. Training deep models typically requires a large amount of data, and overfitting often occurs when dealing with small-sample datasets. We compared four different-sized datasets and found that transfer learning can mitigate the overfitting phenomenon in small-sample scenarios. It partially addresses the issue caused by a small dataset and effectively extracts the value from small-sample data. The specific performance is shown in Table 2. The range from 500 to 5000 represents the number of curves for each class of key labels during model training.