## 3 Methodology
### Data Generation and Preprocessing  
The 3D touch sensors of the Samsung Galaxy A72 device, shown in Figure 2, were experimentally utilized to collect data from touch-based behavioral biometrics. The participant group was selected to include a balanced distribution of gender (15 males and 15 females) and age range (18 to 50 years old), ensuring moderate behavioral diversity within the dataset. Ethical considerations were a fundamental aspect of this study to ensure the responsible collection and use of data. Prior to participation, all individuals provided informed consent, ensuring their awareness and voluntary involvement in the study. To protect participant privacy, all collected data was anonymized, preventing the identification of individual contributors. Every participating person gave ten different pattern recognition samples that comprised typing phrases, unlocking the device, and performing swipe gestures. Each record in the dataset was labeled based on its authentication outcome. For training purposes, valid interactions were encoded as \"1\" and invalid or unauthorized interactions were encoded as \"0\", forming a binary classification setting. Our authentication application, implemented leveraging the Android system of Samsung Galaxy A72, received the incoming data with high resolution that are entered by the participating persons using the 3D touch screen. It is worth mentioning that the collected dataset has been encrypted to prohibit an attacker or unauthorized user from accessing it. In order to verify that this  
Figure 1: The life cycle of our complete framework.  
work is within the conduct research role, it follows the international organization standards in research integrity as it only deals with human participants. The main five extracted features of dynamic pattern recognition that are directly obtained from the 3D touch screen contain the following:  
1. Touch pressure (P): The intensity of pressure applied during interactions.
2. Trajectory curvature (T): The geometric path traced by the finger's movement.
3. Velocity (V): The rate of change in position during swipe gestures.
4. Spatial coordinates (X, Y): The precise position of the finger on the 3D touch screen.
5. Acceleration (A): The change in velocity over time.  
The resulting dataset spans a broad spectrum of user behaviors and scenarios designed to enhance the robustness and adaptability of the proposed system. Noise removal, value normalization, and handling of missing data have been performed during the data preprocessing in order to ensure consistent and trustworthiness of the collected dataset. We also validated the dataset to make sure data reliability and integrity. To keep the collected data reliable and uniform, the mean for numerical features and the mode for nominal features have been leveraged. Note that, the interquartile range (IQR) approach has been used to recognize trajectory data, touch pressure, and velocity and then either exchanged with median values or eliminated for consistency purposes. Also, repeated records or entries have been recognized and then eliminated to avoid bias in the training process, and the entire features have been carefully normalized to a common scale, in order to make sure that they are matchable with each used model.