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Continuous User Authentication Based on Keystroke Dynamics through Neural Network Committee Machines

Continuous User Authentication Based on Keystroke Dynamics through Neural Network Committee Machines

Sérgio Roberto de Lima e Silva Filho, Mauro Roisenberg
Copyright: © 2012 |Pages: 21
ISBN13: 9781613501290|ISBN10: 1613501293|EISBN13: 9781613501306
DOI: 10.4018/978-1-61350-129-0.ch011
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MLA

Filho, Sérgio Roberto de Lima e Silva, and Mauro Roisenberg. "Continuous User Authentication Based on Keystroke Dynamics through Neural Network Committee Machines." Continuous Authentication Using Biometrics: Data, Models, and Metrics, edited by Issa Traore and Ahmed Awad E. Ahmed, IGI Global, 2012, pp. 232-252. https://doi.org/10.4018/978-1-61350-129-0.ch011

APA

Filho, S. R. & Roisenberg, M. (2012). Continuous User Authentication Based on Keystroke Dynamics through Neural Network Committee Machines. In I. Traore & A. Ahmed (Eds.), Continuous Authentication Using Biometrics: Data, Models, and Metrics (pp. 232-252). IGI Global. https://doi.org/10.4018/978-1-61350-129-0.ch011

Chicago

Filho, Sérgio Roberto de Lima e Silva, and Mauro Roisenberg. "Continuous User Authentication Based on Keystroke Dynamics through Neural Network Committee Machines." In Continuous Authentication Using Biometrics: Data, Models, and Metrics, edited by Issa Traore and Ahmed Awad E. Ahmed, 232-252. Hershey, PA: IGI Global, 2012. https://doi.org/10.4018/978-1-61350-129-0.ch011

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Abstract

This chapter proposes an authentication methodology that is both inexpensive and non-intrusive and authenticates users continuously while using a computer keyboard. This proposed methodology uses neural network committee machines. The committee consists of several independent neural networks trained to recognize a behavioral biometric characteristic: user’s typing pattern. Continuous authentication prevents potential attacks when users leave their desks without logging out or locking their computer session. Some experiments were conducted to evaluate and to calibrate the authentication committee. Best results show that a 0% FAR and a 0.15% FRR can be achieved when different thresholds are used in the system for each user. In this proposed methodology, capture system does not need to concern about typing errors in the text. Another feature of this methodology is that new users can be easily added to the system, with no need to re-train all neural networks involved.

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