Development Of A Pressure-Based Typing Biometrics System For User Authentication
Password authentication is the most prevalently used identification system in today’s cyber world. In spite of the popularity of this approach there are many inherent flaws. The password plays the role as the key to a lock; anyone who has it can gain successful access. Additionally, passwords c...
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| Main Author: | |
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| Format: | Monograph |
| Language: | English |
| Published: |
Universiti Sains Malaysia
2005
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| Online Access: | http://eprints.usm.my/57667/1/Development%20Of%20A%20Pressure-Based%20Typing%20Biometrics%20System%20For%20User%20Authentication_Loy%20Chen%20Change.pdf http://eprints.usm.my/57667/ |
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| Summary: | Password authentication is the most prevalently used identification system in today’s
cyber world. In spite of the popularity of this approach there are many inherent flaws.
The password plays the role as the key to a lock; anyone who has it can gain successful
access. Additionally, passwords can be easily cracked, guessed, stolen or deliberately
shared. To minimize the risk of intrusion, keystroke dynamics can be used to
complement this popular authentication method. As the name implies, it is an automated
biometric method that analyzes the way a person types on a keyboard. There have been
a lot of studies on using keystroke timing characteristics to verify the identity of a user.
In this project keystroke pressure (the amount of force exerted on each key pressed) was
employed, and its performance was compared with that of the conventional keystroke
timings-based technique. The project also investigated the use of combined keystroke
pressure and latency for the identification process. In order to measure the forces
exerted during typing, a pressure-sensitive keyboard system was developed. A user
interface that simulates actual login environment was used to collect data from 100
users. All users were requested to enter the same password. Three different
classification methods were applied, namely Logistic Regression (LR), Multilayer
Perceptron (MLP), and Fuzzy ARTMAP (FAM) neural networks. The results were very
encouraging, with a maximum accuracy rate of 93.9% achieved by using FAM.
Keystroke latency gave better results than keystroke pressure, but using both techniques
together yielded the best results, with False Acceptance Rate (FAR) of 0.87% and False
Rejection Rate (FRR) of 4.4%. The experimental results demonstrated that the proposed
methods are promising, and that the keystroke pressure is a viable and practical way to
add more security to conventional typing biometrics authentication system. |
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