A Simple Authentication Method with Multilayer Feedforward Neural Network Using Keystroke Dynamics

Keystroke dynamics is a widely accepted user recognition and verification behavioral biometric, which has been studied nearly for a century. Intrinsically, this biometric is used together with id/password authentication forming multi-factor authentication. There are several anomaly detection algorithms that have been proposed for this task. While some proposals handle this problem with measuring data distance by taking correlation and dependence into account, some models use complex and time-consuming models deep neural networks to train to reach the right approximation. Our paper addresses a simple, accurate and lightweight method for user authentication. We show the effectiveness of our approach through comparisons with existing methods, which have also used the CMU keystroke dynamics benchmark dataset used here too. Using feed forward multilayer neural network with resilient backpropagation, we obtained an Equal Error Rate (ERR) equal to 0.049 for authentication with overall identification accuracy of 94.7%.