Automatic recognition of males and females among web browser users based on behavioural patterns of peripherals usage

Abstract

Purpose The purpose of this paper is to answer the question whether it is possible to recognise the gender of a web browser user on the basis of keystroke dynamics and mouse movements. Design/methodology/approach An experiment was organised in order to track mouse and keyboard usage using a special web browser plug-in. After collecting the data, a number of parameters describing the users’ keystrokes, mouse movements and clicks were calculated for each data sample. Then several machine learning methods were used to verify the stated research question. Findings The experiment showed that it is possible to recognise males and females on the basis of behavioural characteristics with an accuracy exceeding 70 per cent. The best results were obtained while using Bayesian networks. Research limitations/implications The first limitation of the study was the restricted contextual information, i.e. neither the type of web page browsed nor the user activity was taken into account. Another is the narrow scope of the respondent group. Future work should focus on gathering data from more users covering a wider age range and should consider the context. Practical implications Automatic gender recognition could be used in profiling a user to create personalised websites or as an additional feature in automatic identification for security reasons. It might be also considered as a confirmation of declared gender in web-based surveys. Social implications As not all users perceive personalised ads and websites as beneficial, this application requires the analysis of a user perspective to provide value to the consumer without privacy violation. Originality/value Behavioural characteristics, such as mouse movements and keystroke dynamics, have already been used for user authentication and emotion recognition, but applying these data to gender recognition is an original idea.

A Systematic Literature Review on Latest Keystroke Dynamics Based Models

“…KD can also recognise the criminal's age [70], [71], gender [70], [72], handedness [28], [70], hand (s) used [11], [70], typing skill [73], and education level [74], which may help to identify the criminals using a conventional keyboard. It has also been reported that these clues can also be identified using a smartphone as well [75], [76]. The suspect's age, gender, handedness, and hand(s) used can all be used to identify them.…”

Laboratory performance prediction using virtual reality behaviometrics

“…Many research groups report that behavioral patterns observable from the use of a mouse or keyboard can vary from individual to individual and with mood or level of attention. Variation can be so pronounced that mouse usage and keystroke dynamics can be used for authentication and identification [12,13], gender recognition [14], and measuring emotions, stress and engagement in tutoring contexts [15][16][17][18].…”

Automatic recognition of therapy progress among children with autism

“…There are numerous applications incorporating behavioural parameters to authenticate users depending on the way they use input devices 15 , speak 16 or walk 17 etc. They may also be applied to the recognition of emotional states 18 and even gender 19 . In the case of autism behavioural phenomena are noted by human observers.…”