## 4 Experimental Results
### Model Training and Experimental Setup  
To evaluate the effectiveness of dummy power traces against SCAs, DL models were trained and tested using the generated dataset. Three primary architectures were considered: RNN, Bi-RNN, and MLP [31]. These models were selected due to their ability to capture sequential dependencies in power consumption patterns, making them particularly suitable for time-series classification tasks. The dataset underwent preprocessing, including data normalization and augmentation, to enhance model generalization. Controlled noise injection and jitter effects were applied to simulate real-world measurement variations. Training was conducted using a high-performance GPU to ensure efficient computation. Each model was optimized using the Adam optimizer and trained using the cross-entropy loss function. To prevent overfitting, regularization techniques such as dropout and L2 regularization were applied. Specifically, the architecture of each model is as follows:  
* **RNN. ** The unidirectional model begins with a simple RNN layer containing 50 hidden units and configured to return the full output sequence. Its output is flattened, followed by a dropout layer with a rate of 30%, a fully connected layer of 10 neurons that use ReLU activation, and a two-unit Softmax output layer. * **Bi-RNN. ** This architecture mirrors the unidirectional RNN but processes each sequence in both forward and backward directions through 50 units in each direction. The subsequent flattening, dropout, dense, and Softmax layers are identical to those of the RNN. * **MLP. ** The feed-forward network consists of a single hidden layer with one hundred ReLU-activated neurons and a two-unit Softmax output layer. During training, L2 regularization is used to promote generalization. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & \begin{tabular}{c} **Original** \\ **Instruction** \\ \end{tabular} & \
\begin{tabular}{c} **Dummy** \\ **Instruction** \\ \end{tabular} &
\begin{tabular}{c} **Total Entries** \\ \end{tabular} \\ \hline DES & 399,561 & 399,558 & 799,119 \\ Hash & 9870 & 9869 & 19,739 \\ RSA & 6850 & 6847 & 13,697 \\
**Total** & **416,281** & **416,274** & **832,555** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Dataset distribution. Figure 11: Comparison of original and dummy power traces for Hash algorithms. Figure 10: Comparison of original and dummy power traces for DES algorithms. Figure 9: Comparison of original and dummy power traces for RSA algorithms. Figure 12 compares the prediction results of models trained exclusively on original power traces and those of models trained on power traces augmented with dummy traces. The model trained only on original traces exhibits low classification performance, indicating the effectiveness of hiding techniques as a countermeasure against conventional DL-SCA. However, when the DL model is trained on power traces augmented with dummy traces, classification performance improves significantly. This suggests that, although hiding operations may initially serve as an effective hiding strategy, they can be vulnerable to advanced SCA models trained to identify and exploit such patterns. Table 7 highlights the impact of dummy traces on classification performance. The models trained with dummy traces achieved significantly higher accuracy, precision, recall, and F1-score, indicating that the presence of dummy traces improves the model's ability to differentiate between power traces. Conversely, models trained without dummy traces exhibited a drastic drop in performance when evaluated on traces containing dummy traces, indicating poor generalization and highlighting the defensive effectiveness of the hiding technique in obfuscating cryptographic execution patterns. This phenomenon can be viewed as the reverse effect of increasing training data to mitigate overfitting. When dummy traces are introduced only at inference time, the model trained solely on original traces fails to generalize and exhibits behavior similar to overfitting.