Application of Keystroke Dynamics Modelling Techniques to Strengthen the User Identification in the Context of E-commerce

Keystroke dynamics is a biometric technique to identify users based on analysing habitual rhythm patterns in their typing behaviour. In e-commerce, this technique brings benefits to both security and the analysis of patterns of consumer behaviour. This paper focuses on analysing the keystroke dynamics against an e-commerce site for personal identification. This paper is an empirical reinforcement of previous works, with data extracted from realistic conditions that are of most interest for the practical application of modelling keystroke dynamics in free texts. It was a collaborative work with one of the leading e-commerce companies in Latin America. Experimental results showed that it was possible to identify typists with an accuracy of 89% from a sampling of 300 randomly selected users just by reading comment field keystrokes.VII Workshop Seguridad Informática (WSI)Red de Universidades con Carreras en Informática (RedUNCI

Application of Keystroke Dynamics Modelling Techniques to Strengthen the User Identification in the Context of E-commerce

Keystroke dynamics is a biometric technique to identify users based on analysing habitual rhythm patterns in their typing behaviour. In e-commerce, this technique brings benefits to both security and the analysis of patterns of consumer behaviour. This paper focuses on analysing the keystroke dynamics against an e-commerce site for personal identification. This paper is an empirical reinforcement of previous works, with data extracted from realistic conditions that are of most interest for the practical application of modelling keystroke dynamics in free texts. It was a collaborative work with one of the leading e-commerce companies in Latin America. Experimental results showed that it was possible to identify typists with an accuracy of 89% from a sampling of 300 randomly selected users just by reading comment field keystrokes.VII Workshop Seguridad Informática (WSI)Red de Universidades con Carreras en Informática (RedUNCI

Ranking to Learn and Learning to Rank: On the Role of Ranking in Pattern Recognition Applications

The last decade has seen a revolution in the theory and application of machine learning and pattern recognition. Through these advancements, variable ranking has emerged as an active and growing research area and it is now beginning to be applied to many new problems. The rationale behind this fact is that many pattern recognition problems are by nature ranking problems. The main objective of a ranking algorithm is to sort objects according to some criteria, so that, the most relevant items will appear early in the produced result list. Ranking methods can be analyzed from two different methodological perspectives: ranking to learn and learning to rank. The former aims at studying methods and techniques to sort objects for improving the accuracy of a machine learning model. Enhancing a model performance can be challenging at times. For example, in pattern classification tasks, different data representations can complicate and hide the different explanatory factors of variation behind the data. In particular, hand-crafted features contain many cues that are either redundant or irrelevant, which turn out to reduce the overall accuracy of the classifier. In such a case feature selection is used, that, by producing ranked lists of features, helps to filter out the unwanted information. Moreover, in real-time systems (e.g., visual trackers) ranking approaches are used as optimization procedures which improve the robustness of the system that deals with the high variability of the image streams that change over time. The other way around, learning to rank is necessary in the construction of ranking models for information retrieval, biometric authentication, re-identification, and recommender systems. In this context, the ranking model's purpose is to sort objects according to their degrees of relevance, importance, or preference as defined in the specific application.Comment: European PhD Thesis. arXiv admin note: text overlap with arXiv:1601.06615, arXiv:1505.06821, arXiv:1704.02665 by other author

Modulation of neural activity in frontopolar cortex drives reward-based motor learning

The frontopolar cortex (FPC) contributes to tracking the reward of alternative choices during decision making, as well as their reliability. Whether this FPC function extends to reward gradients associated with continuous movements during motor learning remains unknown. We used anodal transcranial direct current stimulation (tDCS) over the right FPC to investigate its role in reward-based motor learning. Nineteen healthy human participants practiced novel sequences of finger movements on a digital piano with corresponding auditory feedback. Their aim was to use trialwise reward feedback to discover a hidden performance goal along a continuous dimension: timing. We additionally modulated the contralateral motor cortex (left M1) activity, and included a control sham stimulation. Right FPC-tDCS led to faster learning compared to lM1-tDCS and sham through regulation of motor variability. Bayesian computational modelling revealed that in all stimulation protocols, an increase in the trialwise expectation of reward was followed by greater exploitation, as shown previously. Yet, this association was weaker in lM1-tDCS suggesting a less efficient learning strategy. The effects of frontopolar stimulation were dissociated from those induced by lM1-tDCS and sham, as motor exploration was more sensitive to inferred changes in the reward tendency (volatility). The findings suggest that rFPC-tDCS increases the sensitivity of motor exploration to updates in reward volatility, accelerating reward-based motor learning

Avances en educción de dinámica de tecleo y el contexto emocional de un individuo aplicando interfaz cerebro computadora

El presente artículo describe avances y objetivos del proyecto durante el último año. Orientado a la articulación entre el patrón biométrico de cadencia de tecleo y la determinación de estados emocionales, el proyecto se enfocará en desarrollar un marco de trabajo que permita determinar personas y las relaciones de las mismas con su estado afectivo. Para el mismo se está trabajando en obtener un banco de datos adquirido con una interfaz cerebro—computadora sobre un individuo al teclado. Este entorno de trabajo permitirá analizar las modelizaciones de las emociones adquiridas y plantear los cambios temporales de ritmos de tecleo de un usuario y analizarlos en función de sus emociones. En este contexto fue desarrollado un prometedor algoritmo de análisis de tecleo en textos libres que permite identificar personas, un robusto set de datos de dinámicas de tecleo y un ambiente de experimentación con el que se recabarán los datos emocionales y dinámicas de tecleo.Eje: Seguridad InformáticaRed de Universidades con Carreras en Informátic

Avances en educción de dinámica de tecleo y el contexto emocional de un individuo aplicando interfaz cerebro computadora

El presente artículo describe avances y objetivos del proyecto durante el último año. Orientado a la articulación entre el patrón biométrico de cadencia de tecleo y la determinación de estados emocionales, el proyecto se enfocará en desarrollar un marco de trabajo que permita determinar personas y las relaciones de las mismas con su estado afectivo. Para el mismo se está trabajando en obtener un banco de datos adquirido con una interfaz cerebro—computadora sobre un individuo al teclado. Este entorno de trabajo permitirá analizar las modelizaciones de las emociones adquiridas y plantear los cambios temporales de ritmos de tecleo de un usuario y analizarlos en función de sus emociones. En este contexto fue desarrollado un prometedor algoritmo de análisis de tecleo en textos libres que permite identificar personas, un robusto set de datos de dinámicas de tecleo y un ambiente de experimentación con el que se recabarán los datos emocionales y dinámicas de tecleo.Eje: Seguridad InformáticaRed de Universidades con Carreras en Informática (RedUNCI