Design and Evaluation of a Pressure-Based Typing Biometric Authentication System

Eltahir, W.E.; Salami, Momoh Jimoh Eyiomika; Ismail, Ahmad Faris; Lai, Weng Kin

doi:10.1155/2008/345047

Cited by 10 publications

(7 citation statements)

References 4 publications

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“…All results were calculated online. The results showed FRR of 3.04 and FAR of 3.75 (Eltahir et al 2008).…”

Section: Combined Latency and Ar Classifiersmentioning

confidence: 94%

“…The hardware layout of the system is unique. It consists of a desktop computer equipped with a data acquisition card, and a specially designed alphanumeric keyboard embedded with pressure sensors (Eltahir et al, 2003). As illustrated in Figure 3, the main hardware components of PBAS are as follows: 1.…”

Section: Pressure Based Typing Biometric Authentication System 21 Symentioning

confidence: 99%

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Design and Evaluation of a Pressure-Based Typing Biometric Authentication System

Eltahir¹,

Salami²,

Ismail³

et al. 2008

EURASIP J Inform Secur

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Keystroke authentication systems available in the market are mostly software-based. This is due to the ease of use as well as the low cost of the mechanism. Any new keystroke www.intechopen.com Design and Evaluation of a Pressure Based Typing Biometric Authentication System 237 authentication system has to consider these factors in the design. Likewise, the system designed for PBAS uses simplified hardware which minimizes the cost of production. The system is designed to be compatible with any type of PC. Moreover, it does not require any external power supply. In general, the system components are low cost and commonly available in the market.

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“…All results were calculated online. The results showed FRR of 3.04 and FAR of 3.75 (Eltahir et al 2008).…”

Section: Combined Latency and Ar Classifiersmentioning

confidence: 94%

Section: Pressure Based Typing Biometric Authentication System 21 Symentioning

confidence: 99%

Design and Evaluation of a Pressure-Based Typing Biometric Authentication System

Eltahir¹,

Salami²,

Ismail³

et al. 2008

EURASIP J Inform Secur

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show abstract

“…keystroke duration, latency between consecutive keystrokes or the force exerted on the keys. Some prior work has been done on the use of keystroke biometrics as a password hardening technique [8][9][10][11]. In addition to the duration between each pair of keystrokes, Obaidat and Sadoun [12] investigated the use of the holding time for each key pressed.…”

Section: Related Work and Motivationmentioning

confidence: 99%

User Identification of Keystroke Biometric Patterns with the Cognitive RAM Weightless Neural Net

Lai

Tan

Soo

et al. 2016

Lecture Notes in Electrical Engineering

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A user identification system which matches the keystroke dynamics of the users with the Cognitive RAM (CogRAM) weightless neural net is discussed in this paper. The keystroke patterns are made up of a common password for all users. While there are several common approaches to represent the users' keystroke patterns, the approach adopted here is based on the force applied to each key. Effectively, they will then constitute a fixed length passkey. In addition, the system was developed based on an 8-bit AVR enhanced, RISC microcontroller. From the experimental results obtained, it can be seen that the identity of the users can be successfully matched just from their keystroke biometric patterns alone.

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“…Research in the most immediate past ( Changshui and Yanhua, 2000 ; Cho et al, 2000 ; Haider et al, 2000 ; Monrose and Rubin, 2000 ; Wong et al, 2001 ; Bergadano et al, 2002 ; D’Souza, 2002 ; Henderson et al, 2002 ; Mantyjarvi et al, 2002 ; Eltahir et al, 2003 , 2004 , 2008 ; Jansen, 2003 ; Nonaka and Kurihara, 2004 ; Peacock et al, 2004 ; Araújo et al, 2005 ; Chang, 2005 ; Lee and Cho, 2005 ; Rodrigues et al, 2005 ; Curtin et al, 2006 ; Hosseinzadeh et al, 2006 ; Lv and Wang, 2006 ; Clarke and Furnell, 2007 ; Hocquet et al, 2007 ; Loy et al, 2007 ; Grabham and White, 2008 ; Lv et al, 2008 ; Saevanee and Bhatarakosol, 2008 , 2009 ; Campisi et al, 2009 ; Hwang et al, 2009a , b ; Killourhy and Maxion, 2009 ; Revett, 2009 ; Nguyen et al, 2010 ; Chang et al, 2011 , 2012 ; Giot et al, 2011 ; Karnan et al, 2011 ; Teh et al, 2011 ; Xi et al, 2011 ) has incorporated newly developed mathematical and data recording techniques – again employing statistical techniques and neural networks, but also attempting to fuse data from multiple parallel sensors. The types and differences between the various mathematical techniques are discussed in the next section.…”

Section: Overview: Authenticationmentioning

confidence: 99%

“…Other than new analysis techniques, novel types of data were also considered and analyzed. For example, existing keyboards were modified to generate a measure of the pressure with which a user presses a single key ( Henderson et al, 2002 ; Eltahir et al, 2003 , 2004 ; Nonaka and Kurihara, 2004 ; Lv and Wang, 2006 ; Hocquet et al, 2007 ; Loy et al, 2007 ) – the aim of which was, again, to increase the veracity of user analyzed identity. Such pressure measurements proved useful in building a more accurate template of users’ unique keystroke patterns.…”

Section: Overview: Authenticationmentioning

confidence: 99%

Keystroke dynamics in the pre-touchscreen era

Ahmad

Szymkowiak

Campbell

2013

Front. Hum. Neurosci.

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Biometric authentication seeks to measure an individual’s unique physiological attributes for the purpose of identity verification. Conventionally, this task has been realized via analyses of fingerprints or signature iris patterns. However, whilst such methods effectively offer a superior security protocol compared with password-based approaches for example, their substantial infrastructure costs, and intrusive nature, make them undesirable and indeed impractical for many scenarios. An alternative approach seeks to develop similarly robust screening protocols through analysis of typing patterns, formally known as keystroke dynamics. Here, keystroke analysis methodologies can utilize multiple variables, and a range of mathematical techniques, in order to extract individuals’ typing signatures. Such variables may include measurement of the period between key presses, and/or releases, or even key-strike pressures. Statistical methods, neural networks, and fuzzy logic have often formed the basis for quantitative analysis on the data gathered, typically from conventional computer keyboards. Extension to more recent technologies such as numerical keypads and touch-screen devices is in its infancy, but obviously important as such devices grow in popularity. Here, we review the state of knowledge pertaining to authentication via conventional keyboards with a view toward indicating how this platform of knowledge can be exploited and extended into the newly emergent type-based technological contexts.

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Design and Evaluation of a Pressure-Based Typing Biometric Authentication System

Cited by 10 publications

References 4 publications

Design and Evaluation of a Pressure-Based Typing Biometric Authentication System

Design and Evaluation of a Pressure-Based Typing Biometric Authentication System

User Identification of Keystroke Biometric Patterns with the Cognitive RAM Weightless Neural Net

Keystroke dynamics in the pre-touchscreen era

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