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Siamese Neural Network for Keystroke Dynamics-Based Authentication on Partial Passwords.

Kamila Lis¹, Ewa Niewiadomska-Szynkiewicz², Katarzyna Dziewulska¹

¹Research and Academic Computer Network, Kolska 12, 01-045 Warsaw, Poland.

Sensors (Basel, Switzerland)

|August 12, 2023

Summary

This study enhances computer security by using typing patterns (keystroke dynamics) for partial password authentication. This method verifies users without extra steps, improving login security and user confidence.

Keywords:

behavioral biometry keyboard keystroke dynamics partial password authentication siamese network

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Area of Science:

Computer Science
Cybersecurity
Biometrics

Background:

Multi-factor authentication enhances computer system security by using multiple identification mechanisms.
Behavioral biometrics, such as typing patterns, offer unobtrusive user verification for increased login security.

Purpose of the Study:

To investigate the feasibility of using partial password typing dynamics to strengthen user authentication.
To assess if keystroke dynamics can be effectively employed for verifying users based on partial password entries.

Main Methods:

A Siamese neural network was utilized for n-shot classification.
User identity was verified using keystroke dynamics derived from static text, specifically partial password typing patterns.
Analysis was performed on experimental results from real-world data.

Main Results:

Keystroke dynamics authentication proved successful for partial password typing patterns.
The proposed method demonstrated effectiveness in verifying user identity based on typing behavior.
Experimental results validated the approach using real data.

Conclusions:

Keystroke dynamics authentication is a viable method for enhancing partial password security.
The research supports the integration of behavioral biometrics into basic authentication processes.
The findings indicate a potential to increase user confidence in authentication systems.