Keystroke Dynamics
Authenticates users by timing patterns in how they type on a keyboard or touchscreen.
Overview
Keystroke dynamics track how users type to provide a behavioral biometric signal for login or continuous authentication.
How it works
- Record timing of key presses and releases.
- Build a profile using statistical or machine-learning models.
- Compare new typing samples to the stored profile.
Common use cases
- Continuous authentication on desktops
- Fraud detection in online banking
- Step-up identity verification
Strengths and limitations
Strengths: Passive and continuous; works with existing hardware.
Limitations: Sensitive to context changes like device or posture.
Key terms
- Dwell time: Duration a key is pressed.
- Flight time: Time between consecutive key presses.
References
Frequently Asked Questions
What features are analyzed?
Hold times, key-to-key latencies, and higher-level timing rhythms across sessions.
How do models handle context changes?
Adaptive or per-device profiles, domain adaptation, and periodic re-enrollment help account for keyboard and posture shifts.
Can bots spoof keystroke patterns?
Scripted automation can mimic simple averages; richer timing distributions and anomaly detection increase resilience.