Continuous Stress Monitoring under Varied Demands Using Unobtrusive Devices

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.This research aims to identify a feasible model to predict a learner’s stress in an online learning platform. It is desirable to produce a cost-effective, unobtrusive and objective method to measure a learner’s emotions. The few signals produced by mouse and keyboard could enable such solution to measure real world individual’s affective states. It is also important to ensure that the measurement can be applied regardless the type of task carried out by the user. This preliminary research proposes a stress classification method using mouse and keystroke dynamics to classify the stress levels of 190 university students when performing three different e-learning activities. The results show that the stress measurement based on mouse and keystroke dynamics is consistent with the stress measurement according to the changes of duration spent between two consecutive questions. The feedforward back-propagation neural network achieves the best performance in the classification

Spectral convergence in tapping and physiological fluctuations: coupling and independence of 1/f noise in the central and autonomic nervous systems.

When humans perform a response task or timing task repeatedly, fluctuations in measures of timing from one action to the next exhibit long-range correlations known as 1/f noise. The origins of 1/f noise in timing have been debated for over 20 years, with one common explanation serving as a default: humans are composed of physiological processes throughout the brain and body that operate over a wide range of timescales, and these processes combine to be expressed as a general source of 1/f noise. To test this explanation, the present study investigated the coupling vs. independence of 1/f noise in timing deviations, key-press durations, pupil dilations, and heartbeat intervals while tapping to an audiovisual metronome. All four dependent measures exhibited clear 1/f noise, regardless of whether tapping was synchronized or syncopated. 1/f spectra for timing deviations were found to match those for key-press durations on an individual basis, and 1/f spectra for pupil dilations matched those in heartbeat intervals. Results indicate a complex, multiscale relationship among 1/f noises arising from common sources, such as those arising from timing functions vs. those arising from autonomic nervous system (ANS) functions. Results also provide further evidence against the default hypothesis that 1/f noise in human timing is just the additive combination of processes throughout the brain and body. Our findings are better accommodated by theories of complexity matching that begin to formalize multiscale coordination as a foundation of human behavior

A new approach to securing passwords using a probabilistic neural network based on biometric keystroke dynamics

Passwords are a common means of identifying an individual user on a computer system. However, they are only as secure as the computer user is vigilant in keeping them confidential. This thesis presents new methods for the strengthening of password security by employing the biometric feature of keystroke dynamics. Keystroke dynamics refers to the unique rhythm generated when keys are pressed as a person types on a computer keyboard. The aim is to make the positive identification of a computer user more robust by analysing the way in which a password is typed and not just the content of what is typed. Two new methods for implementing a keystroke dynamic system utilising neural networks are presented. The probabilistic neural network is shown to perform well and be more suited to the application than traditional backpropagation method. An improvement of 6% in the false acceptance and false rejection errors is observed along with a significant decrease in training time. A novel time sequenced method using a cascade forward neural network is demonstrated. This is a totally new approach to the subject of keystroke dynamics and is shown to be a very promising method The problems encountered in the acquisition of keystroke dynamics which, are often ignored in other research in this area, are explored, including timing considerations and keyboard handling. The features inherent in keystroke data are explored and a statistical technique for dealing with the problem of outlier datum is implemented.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Improving web authentication with keystroke dynamics

Dissertação de mestrado em Engenharia InformáticaAuthentication is frequently referred as the most critical part of a computer system security. Users commonly identify themselves using a combination of username and password, but sometimes this is not enough. Concerning web-based services, attacks like phishing or social engineering can easily result in identity theft. In addition, the widespread use of single sign-on services can seriously increase the consequences of such attacks. In these circumstances strong authentication is mandatory. Strong authentication is often implemented using additional authentication steps or specialized hardware modules, which is not suitable for web-based systems. However, biometrics can used to overcome these limitations. More specifically, behavioural biometrics based on keyboard typing patterns can provide an extra security layer on top of conventional authentication methods, with no additional cost and no impact to the user experience. This work aims to evaluate the feasibility of the implementation of strong authentication on the web using keystroke dynamics. This is carried out through the creation of a web-application prototype, collection of a keystroke dynamics dataset and analysis of various matching algorithms and performance metrics on the collected data.O processo de autenticação é frequentemente referido como a parte mais importante da segurança de um sistema informático. Normalmente, os utilizadores identificam-se utilizando nome de utilizador e palavra-passe, mas este mecanismo nem sempre é suficiente. Considerando serviços baseados na web, ataques como phishing ou engenharia social podem facilmente levar ao roubo de identidade. Para além disso, a utilização crescente de serviços de single sign-on apresenta novos riscos e consequências deste tipo de ataques. Nestas circunstâncias a autenticação forte é fundamental. A autenticação forte é tipicamente implementada por meio de passos adicionais de autenticação ou módulos de hardware especializado, o que não é adequado a sistemas baseados na web. No entanto, biometrias podem ser usadas para ultrapassar estas limitações. Mais especificamente, biometrias comportamentais baseadas em padrões de digitação no teclado podem fornecer um nível de segurança adicional, sem custo acrescido ou impacto na experiência de utilização. Este trabalho tem como objetivo avaliar a viabilidade da implementação de autenticação forte na web usando dinâmica de digitação. Isto é conseguido através da implementação de um protótipo sob a forma de uma aplicação web, captura de dados de digitação e análise de vários algoritmos e métricas de desempenho sobre os dados recolhidos

Keystroke Biometrics Ongoing Competition

This paper presents the first Keystroke Biometrics Ongoing Competition (KBOC) organized to establish a reproducible baseline in person authentication using keystroke biometrics. The competition has been developed using the BEAT platform and includes one of the largest keystroke databases publicly available based on a fixed text scenario. The database includes genuine and attacker keystroke sequences from 300 users acquired in 4 different sessions distributed in a four month time span. The sequences correspond to the user's name and surname and therefore each user comprises an individual and personal sequence. As baseline for KBOC we report the results of 31 different algorithms evaluated according to performance and robustness. The systems have achieved EERs as low as 5.32% and high robustness against multisession variability with drop of performances lower than 1% for probes separated by months. The entire database is publicly available at the competition website

Modelling the dynamics of the piano action: is apparent success real?

International audienceThe kinematics and the dynamics of the piano action mechanism have been much studied in the last 50 years and fairly sophisticated models have been proposed in the last decade. Surprisingly, simple as well as sophisticated models seem to yield very valuable simulations when compared to measurements. We propose here a very simple model, with only 1-degree of freedom, and compare its outcome with force and motion measurements obtained by playing a real piano mechanism. The model, purposely chosen as obviously too simple to be predictive of the dynamics of the grand piano action, appears either as very good or as very bad, depending on which physical quantities are used as the input and output. We discuss the sensitivity of the simulation results to the initial conditions and to noise and the sensitivity of the experimental/simulation comparisons to the chosen dynamical model. It is shown that force-driven simulations with position comparisons, as they are proposed in the literature, do not validate the dynamical models of the piano action. It is suggested that these models be validated with position-driven simulations and force comparisons

Detecting and Modelling Stress Levels in E-Learning Environment Users

A modern Intelligent Tutoring System (ITS) should be sentient of a learner's cognitive and affective states, as a learner’s performance could be affected by motivational and emotional factors. It is important to design a method that supports low-cost, task-independent and unobtrusive sensing of a learner’s cognitive and affective states, to improve a learner's experience in e-learning, as well as to enable personalized learning. Although tremendous related affective computing research were done in this area, there is a lack of empirical research that can automatically measure a learner's stress using objective methods. This research is set to examine how an objective stress measurement model can be developed, to compute a learner’s cognitive and emotional stress automatically using mouse and keystroke dynamics. To ensure the measurement is not affected even if the user switches between tasks, three preliminary research experiments were carried out based on three common tasks during e-learning − search, assessment and typing. A stress measurement model was then built using the datasets collected from the experiments. Three stress classifiers were tested, namely certainty factors, feedforward back-propagation neural network and adaptive neuro-fuzzy inference system. The best classifier was then integrated into the ITS stress inference engine, which is designed to decide necessary adaptation, and to provide analytical information of learners' performances, which include stress levels and learners’ behaviours when answering questions