Personal identification by Keystroke Pattern for login security
This thesis discusses the Neural Network (NN) approach in identifying personnel through keystroke behavior in the login session. The keystroke rhythm that falls in the behavioral biometric has a unique pattern for each individual. Therefore, these heterogeneous data obtained from normal behavior...
محفوظ في:
| المؤلف الرئيسي: | |
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| التنسيق: | أطروحة |
| اللغة: | English English |
| منشور في: |
2001
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| الوصول للمادة أونلاين: | http://psasir.upm.edu.my/id/eprint/8663/1/FSKTM%202001%201%20IR.pdf |
| الوسوم: |
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my-upm-ir.8663 |
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my-upm-ir.86632023-12-18T06:46:29Z Personal identification by Keystroke Pattern for login security 2001-08 Abdullah, Norhayati This thesis discusses the Neural Network (NN) approach in identifying personnel through keystroke behavior in the login session. The keystroke rhythm that falls in the behavioral biometric has a unique pattern for each individual. Therefore, these heterogeneous data obtained from normal behavior users can be used to detect intruders in a computer system. The keystroke behavior was captured in the form of time within the duration between the pressing and releasing of key was recorded during the login session. Ten frequent loggers were chosen for the experiments. The data obtained were presented to NN for pattern learning and classifying the strings of characters. The backpropagation (BP) model was implemented to identify the keystroke patterns for each class.Various architectures were employed in the SP training to achieve the best recognition rate. Several features that influence the network were considered. The experiment involved the slicing of input data and the determination of the number of hidden units. Several other factors such as momentum, learning rate and various weight initialization were used for comparison. Three types of weight initialization were used, including Nguyen-Widrow (NW), Random and Genetic Algorithm (GA). The experiment showed that the recognition of 97% was achieved using NW weight initialization with 10 hidden units. Further experiments with Improved Error Function (IEF) in standard SP has showed better results with 100% recognition on both train and test data set compared to previous experiment. The results of this study were compared with Chambers's (1990) and Obaidat's (1994) work. Chambers used the data set similar to the data used in this experiment and obtained 90.5% recognition through Inductive Learning Classifier method, while Obaidat used standard BP with 6 classes and obtained 97.5% recognition. Computers - Access control - Keystroke timing authentication. Identification numbers, Personal. 2001-08 Thesis http://psasir.upm.edu.my/id/eprint/8663/ http://psasir.upm.edu.my/id/eprint/8663/1/FSKTM%202001%201%20IR.pdf text en public masters Universiti Putra Malaysia Computers - Access control - Keystroke timing authentication. Identification numbers, Personal. Faculty of Computer Science and Information Technology Mahmod, Ramlan English |
| institution |
Universiti Putra Malaysia |
| collection |
PSAS Institutional Repository |
| language |
English English |
| advisor |
Mahmod, Ramlan |
| topic |
Computers - Access control - Keystroke timing authentication. Computers - Access control - Keystroke timing authentication. |
| spellingShingle |
Computers - Access control - Keystroke timing authentication. Computers - Access control - Keystroke timing authentication. Abdullah, Norhayati Personal identification by Keystroke Pattern for login security |
| description |
This thesis discusses the Neural Network (NN) approach in identifying personnel
through keystroke behavior in the login session. The keystroke rhythm that falls in the
behavioral biometric has a unique pattern for each individual. Therefore, these
heterogeneous data obtained from normal behavior users can be used to detect intruders
in a computer system.
The keystroke behavior was captured in the form of time within the duration between
the pressing and releasing of key was recorded during the login session. Ten frequent
loggers were chosen for the experiments. The data obtained were presented to NN for
pattern learning and classifying the strings of characters. The backpropagation (BP)
model was implemented to identify the keystroke patterns for each class.Various architectures were employed in the SP training to achieve the best recognition
rate. Several features that influence the network were considered. The experiment
involved the slicing of input data and the determination of the number of hidden units.
Several other factors such as momentum, learning rate and various weight initialization
were used for comparison. Three types of weight initialization were used, including
Nguyen-Widrow (NW), Random and Genetic Algorithm (GA). The experiment showed
that the recognition of 97% was achieved using NW weight initialization with 10 hidden
units. Further experiments with Improved Error Function (IEF) in standard SP has
showed better results with 100% recognition on both train and test data set compared to
previous experiment.
The results of this study were compared with Chambers's (1990) and Obaidat's (1994)
work. Chambers used the data set similar to the data used in this experiment and
obtained 90.5% recognition through Inductive Learning Classifier method, while
Obaidat used standard BP with 6 classes and obtained 97.5% recognition. |
| format |
Thesis |
| qualification_level |
Master's degree |
| author |
Abdullah, Norhayati |
| author_facet |
Abdullah, Norhayati |
| author_sort |
Abdullah, Norhayati |
| title |
Personal identification by Keystroke Pattern for login security |
| title_short |
Personal identification by Keystroke Pattern for login security |
| title_full |
Personal identification by Keystroke Pattern for login security |
| title_fullStr |
Personal identification by Keystroke Pattern for login security |
| title_full_unstemmed |
Personal identification by Keystroke Pattern for login security |
| title_sort |
personal identification by keystroke pattern for login security |
| granting_institution |
Universiti Putra Malaysia |
| granting_department |
Faculty of Computer Science and Information Technology |
| publishDate |
2001 |
| url |
http://psasir.upm.edu.my/id/eprint/8663/1/FSKTM%202001%201%20IR.pdf |
| _version_ |
1794018768671735808 |