## 3 Methodology
### Receiver
#### 3.2.2 Side Channel Attack Detection  
Here, side channel attacks detected from datasets were collected for training the model for attack detection. * **Dataset:** First, the input data related to side-channel attack detection were collected. The collected data were represented as follows: \[S=\{S_{1},S_{2},S_{3}..,S_{nm}\}\] (24) Here, \((S)\) signifies the collected data, and \((nm)\) represents the total number of data in \((S)\). * Here, \((S)\) were pre-processed to augment the quality of the data. Here, the important steps, such as Missing Value Imputation \((MVI)\), normalization, and normalization were performed. * _Value Imputation_: Here, using the mean formula, the missing values in \((S)\) were imputed for handling the incomplete information. The missing value imputed data were expressed as \[v_{imp}=\frac{\sum S}{nm}\] (25)
Here, \(v_{imp}\) signifies the missing value imputed data. * _Numericalization_: Then, for easier processing and analysis of the data, the values in \(v_{imp}\) were converted into numerical formats. Thus, the humanized data were obtained and are denoted as \(F\). * _Normalization_: The humanized data \(F\) were given as the normalization input for scaling the data to a specific range, typically between 0 and 1 or \(-1\) and 1, based on the minimum and maximum values. \[\widetilde{F}=\frac{F-F_{min}}{F_{Max}-F_{Min}}\] (26) Here, \((\widetilde{F})\) signifies the normalized data, and \((F_{Min})\) and \((F_{Max})\) signify the minimum and maximum values in \(F\), respectively. Thus, the pre-processed data were obtained and denoted as \((\varsigma_{pre})\). * _Feature Extraction_: Now, from \((\varsigma_{pre})\), the features were extracted for effective classification of the side-channel attacks. The features of \((\varsigma_{pre})\), such as side-channel attack traces, were extracted and represented as follows: \[f^{e}=\{f_{1}^{e},f_{2}^{e},...,f_{g}^{e}\}\] (27) Here, \((f^{e})\) signifies the features extracted from \((\varsigma_{pre})\), and \(g\) signifies the total number of features extracted. * _Classification_: The model was trained from \((f^{e})\) with ADS-CNN, to ascertain the presence of a side channel assault. Current Convolutional Neural Networks (CNNs) are seen as proficient in recognizing spatial patterns owing to their convolutional layers, which encapsulate local dependencies and hierarchical feature representations. Nonetheless, the pooling layers in the CNNs diminished the dimensionality of the feature maps by condensing small regions, resulting in the loss of intricate details. Adaptive Depthwise Separable Convolution was employed in the Convolutional Neural Networks to address this issue. This minimized the number of parameters and processing expenses by partitioning the convolution operation into depthwise and pointwise convolutions. When integrated with adaptive mechanisms, this improved the network's capacity to preserve intricate spatial information and reduced the information loss resulting from pooling layers. Figure 2 presents the ADS-CNN classifier. * _Convolution layer_: First, Adaptive Depthwise Separable Convolution was used in the convolutional layer to enhance the network's ability to retain detailed spatial  
Figure 2: Structure of the ADS-CNN classifier. information and mitigate the loss of information. To start with, depthwise convolution was performed for \((f^{e})\), utilizing the subsequent equation:  
\[\hat{D}=\sum_{x=0}^{h^{\prime}}\sum_{y=0}^{h^{\prime\prime}}f^{e}(i+x,u+y,q) \cdot B(x,y,q) \tag{28}\]  
Here, \(\hat{D}\) signifies the output of the depthwise convolution, \(i\) and \(u\) signifies the spatial indices, \(q\) signifies the channel index, \(B\) denotes the depthwise filter, \(h\) and \(h^{\prime}\) are the height and width of the filter, respectively, and \(x\) and \(y\) represent the row and column offsets of the filter, respectively.