Users' Identification through Keystroke Dynamics Based on Vibration Parameters and Keyboard Pressure

Title	Users' Identification through Keystroke Dynamics Based on Vibration Parameters and Keyboard Pressure
Publication Type	Conference Paper
Year of Publication	2017
Authors	Sulavko, A. E., Eremenko, A. V., Fedotov, A. A.
Conference Name	2017 Dynamics of Systems, Mechanisms and Machines (Dynamics)
Date Published	nov
ISBN Number	978-1-5386-1820-2
Keywords	authorisation, Bayes methods, Bayes rule, biometrics (access control), complementary parameters, Correlation, correlation between biometrie attributes, data protection, Dynamics, error probabilities, error statistics, Human Behavior, human factors, keyboard pressure parameters, keyboard vibration, Keyboards, Keys pressure, keystroke analysis, keystroke dynamics, message authentication, Metrics, multilayer perceptrons, perceptron algorithms, probability density, pubcrawl, quadratic form networks, Sensors, special sensors, time characteristics, Time-frequency Analysis, unauthorized access, user identification, vibration parameters, Vibrations, wide artificial neural networks
Abstract	The paper considers an issues of protecting data from unauthorized access by users' authentication through keystroke dynamics. It proposes to use keyboard pressure parameters in combination with time characteristics of keystrokes to identify a user. The authors designed a keyboard with special sensors that allow recording complementary parameters. The paper presents an estimation of the information value for these new characteristics and error probabilities of users' identification based on the perceptron algorithms, Bayes' rule and quadratic form networks. The best result is the following: 20 users are identified and the error rate is 0.6%.
URL	http://ieeexplore.ieee.org/document/8239514/
DOI	10.1109/Dynamics.2017.8239514
Citation Key	sulavko_users_2017