Application of Neural Network in User Authentication for Smart Home System
Security has been an important issue and concern in the smart home systems. Smart home networks consist of a wide range of wired or wireless devices, there is possibility that illegal access to some restricted data or devices may happen. Password-based authentication is widely used to identify a...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
WASET
2009
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| Online Access: | http://ir.unimas.my/id/eprint/17797/ http://ir.unimas.my/id/eprint/17797/1/Application%20of%20Neural%20Network%20in%20User%20authentication%20%28abstract%29.pdf |
| _version_ | 1848838370721005568 |
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| author | Joseph, A. David Bong, Boon Liang Dayang Azra, Awang Mat |
| author_facet | Joseph, A. David Bong, Boon Liang Dayang Azra, Awang Mat |
| author_sort | Joseph, A. |
| building | UNIMAS Institutional Repository |
| collection | Online Access |
| description | Security has been an important issue and concern in the
smart home systems. Smart home networks consist of a wide range of
wired or wireless devices, there is possibility that illegal access to
some restricted data or devices may happen. Password-based
authentication is widely used to identify authorize users, because this
method is cheap, easy and quite accurate. In this paper, a neural
network is trained to store the passwords instead of using verification
table. This method is useful in solving security problems that
happened in some authentication system. The conventional way to
train the network using Backpropagation (BPN) requires a long
training time. Hence, a faster training algorithm, Resilient
Backpropagation (RPROP) is embedded to the MLPs Neural
Network to accelerate the training process. For the Data Part, 200
sets of UserID and Passwords were created and encoded into binary
as the input. The simulation had been carried out to evaluate the
performance for different number of hidden neurons and combination
of transfer functions. Mean Square Error (MSE), training time and
number of epochs are used to determine the network performance.
From the results obtained, using Tansig and Purelin in hidden and
output layer and 250 hidden neurons gave the better performance. As
a result, a password-based user authentication system for smart home
by using neural network had been developed successfully. |
| first_indexed | 2025-11-15T06:54:29Z |
| format | Article |
| id | unimas-17797 |
| institution | Universiti Malaysia Sarawak |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T06:54:29Z |
| publishDate | 2009 |
| publisher | WASET |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | unimas-177972024-01-30T02:47:56Z http://ir.unimas.my/id/eprint/17797/ Application of Neural Network in User Authentication for Smart Home System Joseph, A. David Bong, Boon Liang Dayang Azra, Awang Mat TK Electrical engineering. Electronics Nuclear engineering Security has been an important issue and concern in the smart home systems. Smart home networks consist of a wide range of wired or wireless devices, there is possibility that illegal access to some restricted data or devices may happen. Password-based authentication is widely used to identify authorize users, because this method is cheap, easy and quite accurate. In this paper, a neural network is trained to store the passwords instead of using verification table. This method is useful in solving security problems that happened in some authentication system. The conventional way to train the network using Backpropagation (BPN) requires a long training time. Hence, a faster training algorithm, Resilient Backpropagation (RPROP) is embedded to the MLPs Neural Network to accelerate the training process. For the Data Part, 200 sets of UserID and Passwords were created and encoded into binary as the input. The simulation had been carried out to evaluate the performance for different number of hidden neurons and combination of transfer functions. Mean Square Error (MSE), training time and number of epochs are used to determine the network performance. From the results obtained, using Tansig and Purelin in hidden and output layer and 250 hidden neurons gave the better performance. As a result, a password-based user authentication system for smart home by using neural network had been developed successfully. WASET 2009 Article PeerReviewed text en http://ir.unimas.my/id/eprint/17797/1/Application%20of%20Neural%20Network%20in%20User%20authentication%20%28abstract%29.pdf Joseph, A. and David Bong, Boon Liang and Dayang Azra, Awang Mat (2009) Application of Neural Network in User Authentication for Smart Home System. International Science Index, Computer and Information Engineering, 3 (5). ISSN 2409-0441 https://waset.org/Publication/application-of-neural-network-in-user-authentication-for-smart-home-system/9242 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Joseph, A. David Bong, Boon Liang Dayang Azra, Awang Mat Application of Neural Network in User Authentication for Smart Home System |
| title | Application of Neural Network in User Authentication for Smart Home System |
| title_full | Application of Neural Network in User Authentication for Smart Home System |
| title_fullStr | Application of Neural Network in User Authentication for Smart Home System |
| title_full_unstemmed | Application of Neural Network in User Authentication for Smart Home System |
| title_short | Application of Neural Network in User Authentication for Smart Home System |
| title_sort | application of neural network in user authentication for smart home system |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://ir.unimas.my/id/eprint/17797/ http://ir.unimas.my/id/eprint/17797/ http://ir.unimas.my/id/eprint/17797/1/Application%20of%20Neural%20Network%20in%20User%20authentication%20%28abstract%29.pdf |