Language Independent Gender Identification Through Keystroke Analysis

Purpose – In this work we investigate the feasibility of iden tifying the gender of an author by measuring the keystroke duration when typing a message. Design/methodology/approach – Three classifiers were constructed and tested. We empirically evaluated the effectiveness of the classifiers by using empirical data. We used primary data as well as a publicly available dataset containing keystrokes from a diff erent language to validate the language independence assumption. Findings – The results of this work indicate that it is possible to identify the gender of an author by analyzing keystroke durations with a probability of success in the region of 70%. Research limitations/implications – The proposed approach was validated with a limited number of participants and languages, yet the statistical tests show the significance of the results. However, t his approach will be further tested with other languages. Practical implications – Having the ability to identify the gender of an aut hor of a certain piece of text has value in digital forensics, as the proposed method will be a source of circumstantial evidence for “putting fingers on keyboard” and for arbitrating cases where the true origin of a message needs to be identified. Social implications – If the proposed method is included as part of a text composing system (such as email, and instant messaging applications) it could increase trust toward the applications that use it and may also work as a deterrent for crimes involving forgery. Originality/value – The proposed approach combines and adapts techniques from the domains of biometric authentication and data classification

Age and gender as cyber attribution features in keystroke dynamic-based user classification processes.

Keystroke dynamics are used to authenticate users, to reveal some of their inherent or acquired characteristics and to assess their mental and physical states. The most common features utilized are the time intervals that the keys remain pressed and the time intervals that are required to use two consecutive keys. This paper examines which of these features are the most important and how utilization of these features can lead to better classification results. To achieve this, an existing dataset consisting of 387 logfiles is used, five classifiers are exploited and users are classified by gender and age. The results, while demonstrating the application of these two characteristics jointly on classifiers with high accuracy, answer the question of which keystroke dynamics features are more appropriate for classification with common classifiers

User Attribution Through Keystroke Dynamics-Based Author Age Estimation

Keystroke dynamics analysis has often been used in user authentication. In this work, it is used to classify users according to their age. The authors have extended their previous research in which they managed to identify the age group that a user belongs to with an accuracy of 66.1%. The main changes made were the use of a larger dataset, which resulted from a new volunteer recording phase, the exploitation of more keystroke dynamics features, and the use of a procedure for selecting those features that can best distinguish users according to their age. Five machine learning models were used for the classification, and their performance in relation to the number of features involved was tested. As a result of these changes in the research method, an improvement in the performance of the proposed system has been achieved. The accuracy of the improved system is 89.7%

R2BN: an adaptive model for keystroke-dynamics-based educational level classification

Over the past decade, keystroke-based pattern recognition techniques, as a forensic tool for behavioral biometrics, have gained increasing attention. Although a num- ber of machine learning-based approaches have been proposed, they are limited in terms of their capability to recognize and profile a set of an individual’s characteristics. In addition, up to today, their focus was primarily gender and age, which seem to be more appropriate for commercial applications (such as developing commercial software), leaving out from research other characteristics, such as the educational level. Educational level is an acquired user characteristic, which can improve targeted advertising, as well as provide valuable information in a digital forensic investigation, when it is known. In this context, this paper proposes a novel machine learning model, the randomized radial basis function network, which recognizes and profiles the edu- cational level of an individual who stands behind the keyboard. The performance of the proposed model is evaluated by using the empirical data obtained by recording volunteers’ keystrokes during their daily usage of a computer. Its performance is also compared with other well-referenced machine learning models using our keystroke dynamic datasets. Although the proposed model achieves high accuracy in educational level prediction of an unknown user, it suffers from high computational cost. For this reason, we examine ways to reduce the time that is needed to build our model, including the use of a novel data condensation method, and discuss the tradeoff between an accurate and a fast prediction. To the best of our knowledge, this is the first model in the literature that predicts the educational level of an individual based on the keystroke dynamics information only

Photoluminescence study of erbium in silicon with a free-electron laser

The influence of intense infrared (IR) radiation in the range 7-17 μm on the photoluminescence (PL) of erbium in silicon has been investigated. To excite the PL a pulsed Nd:YAG laser operating in the visible with a wavelength of 532 nm has been used. The infrared beam was generated by a free-electron laser. In the experiment the intensity and decay kinetics of the low-temperature PL of Er-doped silicon were monitored as a function of the wavelength of the quenching beam and its delay with respect to the excitation pulse of the visible-light laser. The experiments show quenching of the PL by the IR pulse only at delays shorter than approximately 250 μs The result is interpreted as dissociation of the Er-related bound-exciton (BE) state whose effective lifetime is then estimated as approximately 100 μs. A special quenching feature for λ≈ 12.5 μm is detected indicating a possible "back-transfer" mechanism involving the excited Er state. For still longer delay times a small transient increase of Er PL is observed