Generation of keyboard handwriting during user authentication on mobile devices

Objectives. This article discusses a new way of generating keyboard handwriting using a touch keyboard for authentication in currently existing mobile systems.Methods. Due to the insufficient reliability of single password authentication, the proposal is to use it in combination with characteristics which correspond to handwriting on mobile devices. This article demonstrates the possibility of using individual user characteristics in the formulation of keyboard handwriting on devices with touch keyboards. The type of keyboard used affects the characteristics of keyboard handwriting, so this aspect can be used to improve password authentication reliability. The authentication process in the information environment can be supplemented with data on the nature of the impact on a touch keyboard. The use of the built-in 3D Touch function is also of interest. This is available when working on mobile devices and appliances equipped with a touch keyboard. The paper demonstrates that the use of one parameter only is insufficient for accurate authentication. The study proposes a method of determining an acceptable error range for both the touch force and the intermediate interval during authentication. For this purpose, the Laplace function which formulates the interval of each characteristic depending on the required probability of user recognition is used.Results. Touch force and the intermediate interval are sufficient to obtain the necessary characteristics, in order to formulate a refined user portrait depending on the user’s keyboard handwriting. Experimental statistics are given separately for an average sample of three different users depending on touch force. They also provide the results of authentication when using both standard deviations of pressing and the intervals when using the touch keyboard for the iOSXcode platform.Conclusions. The conclusion relates to the possibility of user authentication by keyboard handwriting, formulated on the basis of both the touch force on the keyboard symbols and intervals between pressing. Using the values of the sample mean and standard deviations allows authentication according to the required recognition probability.