Web-Based Benchmark for Keystroke Dynamics Biometric Systems: A Statistical Analysis

Most keystroke dynamics studies have been evaluated using a specific kind of dataset in which users type an imposed login and password. Moreover, these studies are optimistics since most of them use different acquisition protocols, private datasets, controlled environment, etc. In order to enhance the accuracy of keystroke dynamics' performance, the main contribution of this paper is twofold. First, we provide a new kind of dataset in which users have typed both an imposed and a chosen pairs of logins and passwords. In addition, the keystroke dynamics samples are collected in a web-based uncontrolled environment (OS, keyboards, browser, etc.). Such kind of dataset is important since it provides us more realistic results of keystroke dynamics' performance in comparison to the literature (controlled environment, etc.). Second, we present a statistical analysis of well known assertions such as the relationship between performance and password size, impact of fusion schemes on system overall performance, and others such as the relationship between performance and entropy. We put into obviousness in this paper some new results on keystroke dynamics in realistic conditions.Comment: The Eighth International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIHMSP 2012), Piraeus : Greece (2012

Strengthening e-banking security using keystroke dynamics

This paper investigates keystroke dynamics and its possible use as a tool to prevent or detect fraud in the banking industry. Given that banks are constantly on the lookout for improved methods to address the menace of fraud, the paper sets out to review keystroke dynamics, its advantages, disadvantages and potential for improving the security of e-banking systems. This paper evaluates keystroke dynamics suitability of use for enhancing security in the banking sector. Results from the literature review found that keystroke dynamics can offer impressive accuracy rates for user identification. Low costs of deployment and minimal change to users modus operandi make this technology an attractive investment for banks. The paper goes on to argue that although this behavioural biometric may not be suitable as a primary method of authentication, it can be used as a secondary or tertiary method to complement existing authentication systems

Keystroke dynamics as signal for shallow syntactic parsing

Keystroke dynamics have been extensively used in psycholinguistic and writing research to gain insights into cognitive processing. But do keystroke logs contain actual signal that can be used to learn better natural language processing models? We postulate that keystroke dynamics contain information about syntactic structure that can inform shallow syntactic parsing. To test this hypothesis, we explore labels derived from keystroke logs as auxiliary task in a multi-task bidirectional Long Short-Term Memory (bi-LSTM). Our results show promising results on two shallow syntactic parsing tasks, chunking and CCG supertagging. Our model is simple, has the advantage that data can come from distinct sources, and produces models that are significantly better than models trained on the text annotations alone.Comment: In COLING 201

Development of the Keystroke Dynamics Recognition System

The paper is related to creating an algorithm for keystroke dynamics recognition and development of software, which is able to identify users according to their keystroke dynamics. Different characteristics of keystroke dynamics are considered. Probabilistic-statistical methods are compared with neural network algorithms for recognition. The algorithm for recognition was created and implemented. The software was tested with the help of some users. Their keystroke dynamics was analyzed in order to determine an efficiency of the created algorithm

Research of the influence of learning sample size on the accuracy of keystroke dynamics authentication on mobile devices

Keystroke dynamics is a well-investigated behavioral biometric based on the way and rhythm. In this paper, I present a survey of the stability of keystroke dynamics on mobile devices. It contains an analysis of the statistical features users' keystroke dynamics to show how different they are. I also show how a classification accuracy depends on the size of the learning sample

Development of the keystroke dynamics recognition system

The paper is related to creating an algorithm for keystroke dynamics recognition and development of software, which is able to identify users according to their keystroke dynamics. Different characteristics of keystroke dynamics are considered. Probabilistic-statistical methods are compared with neural network algorithms for recognition. The algorithm for recognition was created and implemented. The software was tested with the help of some users. Their keystroke dynamics was analyzed in order to determine an efficiency of the created algorithm