Abstract
Popular biometric security technologies include fingerprint and iris recognition systems. These technologies are extremely accurate because the patterns associated with an individual's finger or eye are very unique and static. However, when these technologies are used for physical access control they inform the potential adversary that specific characteristics are required to gain access. Behaviometrics aims to develop new strategies to enhance physical security via covert monitoring of distinct behavioral patterns. This research presents a novel stand-alone behaviometric prototype that incorporates standard password security with unique pressure characteristics to covertly analyse individual typing patterns. The prototype is evaluated under a controlled setting with 62 human subjects and nine classification algorithms. The kNN algorithm produced the highest classification rate of 94%. This research is one of the few papers that empirically substantiates biometric performance with a large-scale human subject trial, and also identifies several critical design considerations that impact classifier performance.
| Original language | English |
|---|---|
| Pages (from-to) | 249-270 |
| Number of pages | 22 |
| Journal | International Journal of Biometrics |
| Volume | 7 |
| Issue number | 3 |
| DOIs | |
| State | Published - 1 Jan 2015 |
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Keywords
- Biometrics
- Classification
- Keystroke analysis
- Pattern recognition
- Physical security
- Typing dynamics
Cite this
}
An embedded system for extracting keystroke patterns using pressure sensors. / Leberknight, Christopher S.; Recce, Michael L.
In: International Journal of Biometrics, Vol. 7, No. 3, 01.01.2015, p. 249-270.Research output: Contribution to journal › Article
TY - JOUR
T1 - An embedded system for extracting keystroke patterns using pressure sensors
AU - Leberknight, Christopher S.
AU - Recce, Michael L.
PY - 2015/1/1
Y1 - 2015/1/1
N2 - Popular biometric security technologies include fingerprint and iris recognition systems. These technologies are extremely accurate because the patterns associated with an individual's finger or eye are very unique and static. However, when these technologies are used for physical access control they inform the potential adversary that specific characteristics are required to gain access. Behaviometrics aims to develop new strategies to enhance physical security via covert monitoring of distinct behavioral patterns. This research presents a novel stand-alone behaviometric prototype that incorporates standard password security with unique pressure characteristics to covertly analyse individual typing patterns. The prototype is evaluated under a controlled setting with 62 human subjects and nine classification algorithms. The kNN algorithm produced the highest classification rate of 94%. This research is one of the few papers that empirically substantiates biometric performance with a large-scale human subject trial, and also identifies several critical design considerations that impact classifier performance.
AB - Popular biometric security technologies include fingerprint and iris recognition systems. These technologies are extremely accurate because the patterns associated with an individual's finger or eye are very unique and static. However, when these technologies are used for physical access control they inform the potential adversary that specific characteristics are required to gain access. Behaviometrics aims to develop new strategies to enhance physical security via covert monitoring of distinct behavioral patterns. This research presents a novel stand-alone behaviometric prototype that incorporates standard password security with unique pressure characteristics to covertly analyse individual typing patterns. The prototype is evaluated under a controlled setting with 62 human subjects and nine classification algorithms. The kNN algorithm produced the highest classification rate of 94%. This research is one of the few papers that empirically substantiates biometric performance with a large-scale human subject trial, and also identifies several critical design considerations that impact classifier performance.
KW - Biometrics
KW - Classification
KW - Keystroke analysis
KW - Pattern recognition
KW - Physical security
KW - Typing dynamics
UR - http://www.scopus.com/inward/record.url?scp=84942855018&partnerID=8YFLogxK
U2 - 10.1504/IJBM.2015.071948
DO - 10.1504/IJBM.2015.071948
M3 - Article
AN - SCOPUS:84942855018
VL - 7
SP - 249
EP - 270
JO - International Journal of Biometrics
JF - International Journal of Biometrics
SN - 1755-8301
IS - 3
ER -