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2020 | OriginalPaper | Buchkapitel

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

verfasst von : Ahmet Melih Gedikli, Mehmet Önder Efe

Erschienen in: Pattern Recognition and Artificial Intelligence

Verlag: Springer International Publishing

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Abstract

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%.

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Vorheriges Kapitel Room-Level Indoor Localization with Artificial Neural Networks

Nächstes Kapitel A Comparative Study of ANN Tuning Methods for Multiclass Daily Activity and Fall Recognition

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Titel: A Simple Authentication Method with Multilayer Feedforward Neural Network Using Keystroke Dynamics
verfasst von: Ahmet Melih Gedikli
Mehmet Önder Efe
Verlag: Springer International Publishing
Buch: Pattern Recognition and Artificial Intelligence
Print ISBN: 978-3-030-37547-8

Electronic ISBN: 978-3-030-37548-5

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-030-37548-5_2

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