# Siberia: A Self-Sovereign Identity and Multi-Factor Authentication Framework for Industrial Access
## 1 Introduction
### Behavioral Biometrics  
CA has emerged as a promising approach to enhancing digital security by verifying user identity throughout a session, rather than relying on a single login event. This method enables the detection of anomalous behavior and helps prevent unauthorized access or account takeover during active sessions. A core component of CA is behavioral biometrics, which models distinctive user habits such as typing patterns, touch dynamics, or device usage that are difficult to mimic or steal. Unlike physiological biometrics, behavioral data can be collected passively and unobtrusively through everyday interactions with smartphones, computers, or other devices [46; 47]. Behavioral biometrics rely on embedded sensors (e.g., accelerometers, gyroscopes, touchscreens, and microphones) to capture temporal signals that reflect unique user pat terms. These signals are processed and transformed into features used to train supervised machine learning models that distinguish between legitimate users and impostors. In industrial settings aligned with Industry 4.0, high accuracy has been achieved by analyzing keyboard and mouse usage using models like Random Forest and Gradient Boosting [48]. Similarly, smartphones and smartwatches use motion data with algorithms such as Support Vector Machine (SVM), K-Nearest Neighbors (KNN), or Convolutional Neural Network (CNN) to enable efficient and low-latency CA [47]. Although a wide range of techniques is employed in CA systems, balancing security, usability, and privacy remains a challenge [46]. Ensuring robustness against attacks and adaptability to behavioral changes often requires online learning or periodic model retraining. Additionally, the incorporation of contextual information (e.g., time of day and device location) is being explored to improve classification reliability [49]. Ultimately, the synergy between behavioral biometrics and machine learning enhances the precision of identity verification while enabling a seamless user experience, making CA a compelling solution for securing modern digital ecosystems. The effectiveness of CA systems based on behavioral biometrics depends heavily on the quality, richness, and representativeness of the data used to train and evaluate the models. Among the most relevant behavioral signals for this purpose are touch dynamics, keystroke patterns, inertial sensors (e.g., accelerometers, gyroscopes, and magnetometers), and contextual usage patterns. These signals enable detailed modeling of how users physically interact with their devices. The Hand Movement, Orientation, and Grasp (HMOG) dataset is one of the main references in the field of behavioral biometrics. It comprises a set of features obtained from hand micro-movements recorded by smartphone motion sensors while users perform everyday activities such as reading, writing, or walking [50]. This set includes data from more than 100 participants in different physical conditions, both at rest and in motion, making it a key tool for evaluating the robustness of CA systems. Accelerometer, gyroscope, and magnetometer sensors record high-frequency synchronized data, enabling the extraction of fine-grained temporal characteristics. Using datasets such as HMOG, recent studies have explored deep learning techniques to automatically extract representations from raw behavioral signals. A study highlighted in [51] evaluated models such as CNNs and Long Short-Term Memory (LSTM) networks, demonstrating their effectiveness in capturing both temporal dependencies and spatial correlations in data obtained from smartphone sensors. These architectures were tested with data gathered during real-world usage sessions and showed strong generalization across different users and scenarios. The study also emphasized the importance of using signals with high temporal resolution and sufficient duration to train robust models and prevent overfitting. The adoption of deep architectures such as CNNs and LSTMs has advanced the field of CA based on behavioral biometrics.