Keystroke dynamics is a behavioral biometric technique that identifies individuals through their typing rhythm, using temporal features such as dwell time and flight time. This workshop offers a practical hands-on introduction to building keystroke-based authentication systems using entirely open-source tools.
Participants will interact with a Python-based keystroke dynamics lab that captures live typing behavior, extracts biometric timing features, and applies statistical similarity metrics for verification and identification. The session explores how behavioral biometrics can support continuous authentication, anomaly detection, and privacy-preserving identity verification.
Through guided experimentation, attendees will collect their own typing samples, compare biometric signatures, evaluate authentication performance using multiple similarity metrics, and explore how synthetic data generation techniques such as SMOTE and SDV can improve classification robustness. The workshop concludes with multiclass Random Forest identification experiments and discussion of practical applications in cybersecurity and human-centered authentication.
Participants will leave with a reproducible open-source framework, practical understanding of behavioral biometrics, and direct experience designing and evaluating keystroke-based authentication systems.
