Gene

Transformation (genetics)

Process (computing)

Computer security

Modality (human–computer interaction)

Operating system

Biochemistry

Computer science

Word error rate

S/KEY

Pattern recognition (psychology)

Password

Artificial intelligence

Chemistry

Authentication (law)

Keystroke logging

Artificial neural network

Biometrics

Keystroke dynamics

Convolutional neural network

Human–computer interaction

Machine learning

Speech recognition

Keystroke dynamics is one solution to enhance the security of password authentication without adding any disruptive handling for users. Industries are looking for more security without impacting too much user experience. Considered as a friction-less solution, keystroke dynamics is a powerful solution to increase trust during user authentication without adding charge to the user. In this paper, we address the problem of user authentication considering the keystroke dynamics modality. We proposed a new approach based on the conversion of behavioral biometrics data (time series) into a 3D image. This transformation process keeps all the characteristics of the behavioral signal. The time series do not receive any filtering operation with this transformation and the method is bijective. This transformation allows us to train images based on convolutional neural networks. We evaluate the performance of the authentication system in terms of Equal Error Rate (EER) on a significant dataset and we show the efficiency of the proposed approach on a multi-instance system.

Keystroke Dynamics based User Authentication using Deep Learning Neural Networks