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Title: Design and evaluation of a pressure based typing biometric authentication system
Authors: Salami, Momoh-JImoh E.
Eltahir, Wasil
Ali, Hashimah
Keywords: Pressure based typing
Biometric authentication system
Issue Date: 21-Oct-2011
Publisher: IntechOpen
Citation: Salami, M. J., Eltahir, W., & Ali, H. (2011). Design and evaluation of a pressure based typing biometric authentication system. In Biometric Systems, Design and Applications. IntechOpen.
Abstract: Although a variety of authentication devices to verify a user’s identity are in use today for computer access control, passwords have been and probably would remain the preferred method. Password authentication is an inexpensive and familiar paradigm that most operating systems support. However, this method is vulnerable to intruder access. This is largely due to the wrongful use of passwords by many users and to the unabated simplicity of the mechanism which makes such system susceptible to unsubstantiated intruder attacks. Methods are needed, therefore, to either enhance or reinforce existing password authentication techniques. There are two possible approaches to achieve this, namely by measuring the time between consecutive keystrokes “latency” or measuring the force applied on each keystroke. The pressure-based biometric authentication system (PBAS) has been designed to combine these two approaches so as to enhance computer security. PBAS employs force sensors to measure the exact amount of force a user exerts while typing. Signal processing is then carried out to construct a waveform pattern for the password entered. In addition to the force, PBAS measures the actual timing traces, which are often referred to as “latency”. Two approaches to construct user typing pattern have been implemented with PBAS. First approach utilizes a waveform acquired for user keystroke pressure along with time between each keystroke “latency“ to create a unique user password typing pattern for authentication. An auto-regressive (AR) classifier is used for the pressure pattern, while a latency classifier is used for the time between keystrokes. The results of both classifiers are combined to authenticate the user typing pattern. The second approach combines the pressure and latency by creating a pattern of peak keystroke force and latency. By combining the force and time features other classifiers have been tested with PBAS, namely support vector machines (SVM), artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS). Figure 1 illustrates how these classifiers are integrated to develop the system. As compared to conventional keystroke biometric authentication systems, PBAS has employed a new approach by constructing a waveform pattern for the keystroke password. This pattern provides a more dynamic and consistent biometric characteristics of the user. It also eliminates the security threat posed by breaching the system through online network as the access to the system is only possible through the pressure sensor reinforced keyboard “biokeyboard”.
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