# Research on Side-Channel Analysis Based on Deep Learning with Different Sample Data
## 6 Conclusions  
Since deep learning can learn data directly from raw power trajectories, there is no need to rely on artificially designed features or assumptions. Further, the side-channel analysis based on deep learning can directly predict the target intermediate value through the training data set, further simplifying the attack design and reducing the requirements for professional knowledge in specific fields. Based on the above two points, deep learning technology can study the SCA.  
By combining the ChipWhisperer experimental platform with the CW308T-STM demo board, this paper extracts the power trace in the encryption process of the unmasked AES-128 cryptographic algorithm and takes it as the experimental data set. Using the established MLP, CNN, RNN, and LSTM models, this paper trains and tests from small data set samples to sufficient data sets and data samples of different scales, and the quantitative experiments of each deep learning model in SCA are deeply studied to evaluate the effectiveness of each deep learning model for subsequent improvement. The experimental results show that the CNN model has better comprehensive performance among all models under the existing data set. From the visualization results of model accuracy and loss, it can be fully demonstrated that the accuracy of its classifier is very high, the loss is very low, and the stability of the model is better. During the whole training process, the experimental curve of the CNN model is smooth, and there is no violent jitter, which means it has stronger robustness. The training time is also short, and the rank value of the side-channel attack is very low. Only two tracks are needed to recover the correct key byte information.  
However, compared to the ChipWhisperer experimental platform, the actual test environment has low signal-to-noise ratio data sets or other more complex interference situations. Therefore, to improve the security protection capability of cryptographic equipment, it is necessary to further study the data sets with less obvious information characteristics, and to thus analyze and evaluate the comprehensive performance of each deep learning model in SCA. At the same time, given the complexity of the attack process and implementation details in the SCA experiment, the research direction of the future should investigate the possibility of using statistical testing, computing information entropy, and other formal methods to automatically analyze and evaluate the strength of the encryption system against a side-channel attack.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Data Size** & **Train\_Acc** & **Train\_Loss** & **Test\_Acc** & **Test\_Loss** & **Time (s)** & **Param\#** & **Rank** \\ \hline
5k & 0.1932 & 2.6546 & 0.1780 & 2.8335 & 68.0 & 106,996 & 3 \\
10k & 0.2820 & 2.2234 & 0.2770 & 2.2410 & 128.0 & 106,996 & 2 \\
20k & 0.3826 & 1.8559 & 0.3695 & 1.9201 & 255.0 & 106,996 & 3 \\
30k & 0.3954 & 1.7949 & 0.3863 & 1.8032 & 386.0 & 106,996 & 2 \\
40k & 0.4671 & 1.5768 & 0.3718 & 2.0258 & 511.2 & 106,996 & 2 \\ \hline \end{tabular}
\end{table}
Table 7: Specific parameters and experimental results of the LSTM model.  
**Author Contributions:** Conceptualization, L.C. and Y.W.; methodology, L.C.; validation, L.C. and Y.W.; formal analysis, L.C. and S.H.; investigation, X.P.; resources, Y.W.; data curation, X.P.; writing--original draft preparation, L.C. and S.H.; writing--review and editing, L.C. and Y.W.; supervision, X.P.; project administration, Y.W.; funding acquisition, Y.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Research on the Security of AES and Related Cryptographic Algorithms in the General Project of the Shaanxi Provincial Foundation Fund (2021)JM-254).  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.