Keystroke dynamics-the analysis of typing rhythms to discriminate among users-has been proposed for detecting impostors (i.e., both insiders and external attackers). Since many anomaly-detection algorithms have been proposed for this task, it is natural to ask which are the top performers (e.g., to identify promising research directions). Unfortunately, we cannot conduct a sound comparison of detectors using the results in the literature because evaluation conditions are inconsistent across studies. Our objective is to collect a keystroke-dynamics data set, to develop a repeatable evaluation procedure, and to measure the performance of a range of detectors so that the results can be compared soundly. We collected data from 51 subjects typing 400 passwords each, and we implemented and evaluated 14 detectors from the keystroke-dynamics and pattern-recognition literature. The three top-performing detectors achieve equal-error rates between 9.6% and 10.2%. The results-along with the shared data and evaluation methodology-constitute a benchmark for comparing detectors and measuring progress.

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Comparing anomaly-detection algorithms for keystroke dynamics

Dependence on computers to store and process sensitive information has made it necessary to secure them from intruders. A behavioral biometric such as keystroke dynamics which makes use of the typing cadence of an individual can be used to strengthen existing security techniques effectively and cheaply. Due to the ballistic (semi-autonomous) nature of the typing behavior it is difficult to impersonate, making it useful as abiometric. Therefore in this paper, we provide a basic background of the psychological basis behind the use of keystroke dynamics. We also discuss the data acquisition methods, approaches and the performance of the methods used by researchers on standard computer keyboards. In this survey, we find that the use and acceptance of this biometric could be increased by development of standardized databases, assignment of nomenclature for features, development of common data interchange formats, establishment of protocols for evaluating methods, and resolution of privacy issues.

Biometric Authentication and Identification using Keystroke Dynamics: A Survey

Research on keystroke dynamics biometrics has been increasing, especially in the last decade. The main motivation behind this effort is due to the fact that keystroke dynamics biometrics is economical and can be easily integrated into the existing computer security systems with minimal alteration and user intervention. Numerous studies have been conducted in terms of data acquisition devices, feature representations, classification methods, experimental protocols, and evaluations. However, an up-to-date extensive survey and evaluation is not yet available. The objective of this paper is to provide an insightful survey and comparison on keystroke dynamics biometrics research performed throughout the last three decades, as well as offering suggestions and possible future research directions.

https://downloads.hindawi.com/journals/tswj/2013/408280.pdf

A survey of keystroke dynamics biometrics

This paper uses a static keystroke dynamics in user authentication. The inputs are the key down and up times and the key ASCII codes captured while the user is typing a string. Four features (key code, two keystroke latencies, and key duration) were analyzed and seven experiments were performed combining these features. The results of the experiments were evaluated with three types of user: the legitimate, the impostor and the observer impostor users. The best results were achieved utilizing all features, obtaining a false rejection rate of 1.45% and a false acceptance rate of 1.89%. This approach can be used to improve the usual login-password authentication when the password is no more a secret. This paper innovates using four features to authenticate users.

User authentication through typing biometrics features

The computer keyboard is one of the most common, reliable, accessible, and effective tools used for human–machine interfacing and information exchange. Although keyboards have been used for hundred...

Personalized keystroke dynamics for self-powered human--machine interfacing.

This paper uses a static typing biometrics in user authentication The inputs are the key down and up times and the key ASCII codes captured while the user is typing a string Four features (key code, two keystroke latencies and key duration) were analyzed, and, seven experiments were performed combining these features The results of the experiments were evaluated involving three types of user: the legitimate, the impostor and the observer impostor users The best results were achieved utilizing all features, obtaining a false rejection rate (FRR) of 145% and a false acceptance rate (FAR) of 189% This approach can be used to improve the usual login-password authentication when the password is no more a secret This paper innovates using the combination of four features to authenticate users

User Authentication through Typing Biometrics Features

A Fuzzy Logic Approach in Typing Biometrics User Authentication.

Uma metodologia para autenticação pessoal baseada em dinamica da digitação

Resumen--Este trabajo de investigacion presenta un metodo biometrico de autentificacion para acceso a sistemas informaticos basado en el reconocimiento del patron de tecleo de sus usuarios. El metodo emplea cuatro caracteristicas de tecleo: el codigo de la tecla, dos tipos de tiempos entre pulsaciones de teclas consecutivas y el tiempo que cada tecla permanece presionada. Siete experimentos fueron realizados utilizando las caracteristicas de la secuencia de caracteres en conjunto con un clasificador difuso. Los resultados de los experimentos son evaluados en tres situaciones: usuario legitimo, impostor e impostor especialista. Como resultado de los experimentos se obtuvo tasas de errores de falso rechazo de 3.4 % y de falsa aceptacion de 2.9 %.

http://ieeexplore.ieee.org/iel5/9907/31502/01468645.pdf

Lívia C. F. Araújo

Papers

User authentication through typing biometrics features

User Authentication through Typing Biometrics Features

A Fuzzy Logic Approach in Typing Biometrics User Authentication.

Uma metodologia para autenticação pessoal baseada em dinamica da digitação

Autentificación Personal por Dinámica de Tecleo Basada en Lógica Difusa