Identification potential of keyboard handwriting considering vibration parameters and force keystrokes
The article considers the problem of data protection from unauthorized access by means of user identification by keyboard handwriting. The estimation of informativeness of different features that characterize the keyboard handwriting of subjects, including the dynamics of change in pressure when you press the keys and keyboard settings vibration. The category of new features, based on using of wavelet transform Daubechies D6 to function of the pressure fingers on the keys and keyboard functions of vibration while typing, was proposed. The laws of distribution of basic and additional features of keyboard handwriting were determined. To form the base of biometric samples a keyboard was designed with the use of special sensors. The estimation of the correlation dependence of features was made. It is determined that the correlation between basic features (temporal characteristics of keystrokes) and additional features (pressure on the keys and the keyboard vibration) in more than 80% of cases is weak. Thus, in the proposed new attributes contain information about the subject. An assessment of the probability of identification errors based on the Bayesian strategy using the various features of the spaces was made. It is found that additional features can reduce the average number of errors is more than 7 times.