An Agent-Based Modeling Approach to Free-Text Keyboard Dynamics for Continuous Authentication

Continuous authentication systems leveraging free-text keyboard dynamics offer a promising additional layer of security in a multifactor authentication setup that can be used in a transparent way with no impact on user experience. This study investigates the efficacy of behavioral biometrics by employing an Agent-Based Model (ABM) to simulate diverse typing profiles across mechanical and membrane keyboards. Specifically, we generated synthetic keystroke data from five unique agents, capturing features related to dwell time, flight time, and error rates within sliding 5-second windows updated every second. Two machine learning approaches, One-Class Support Vector Machine (OC-SVM) and Random Forest (RF), were evaluated for user verification. Results revealed a stark contrast in performance: while One-Class SVM failed to differentiate individual users within each group, Random Forest achieved robust intra-keyboard user recognition (Accuracy > 0.7) but struggled to generalize across keyboards for the same user, highlighting the significant impact of keyboard hardware on typing behavior. These findings suggest that: (1) keyboard-specific user profiles may be necessary for reliable authentication, and (2) ensemble methods like RF outperform One-Class SVM in capturing fine-grained user-specific patterns.

Voice Analysis for Stress Detection and Application in Virtual Reality to Improve Public Speaking in Real-time: A Review

Stress during public speaking is common and adversely affects performance and self-confidence. Extensive research has been carried out to develop various models to recognize emotional states. However, minimal research has been conducted to detect stress during public speaking in real time using voice analysis. In this context, the current review showed that the application of algorithms was not properly explored and helped identify the main obstacles in creating a suitable testing environment while accounting for current complexities and limitations. In this paper, we present our main idea and propose a stress detection computational algorithmic model that could be integrated into a Virtual Reality (VR) application to create an intelligent virtual audience for improving public speaking skills. The developed model, when integrated with VR, will be able to detect excessive stress in real time by analysing voice features correlated to physiological parameters indicative of stress and help users gradually control excessive stress and improve public speaking performance