User authentication using neural network in smart home

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 au...

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Main Author: Jee,, Tze Ling
Format: Final Year Project Report
Language:English
English
Published: Universiti Malaysia Sarawak, (UNIMAS) 2009
Subjects:
Online Access:http://ir.unimas.my/id/eprint/6549/1/User%20Authentication%20Using%20Neural%20Network%20in%20Smart%20Home%2824%20pgs%29.pdf
http://ir.unimas.my/id/eprint/6549/8/User%20Authentication%20Using%20Neural%20Network%20in%20Smart%20Home%28OCR%29.pdf
http://ir.unimas.my/id/eprint/6549/
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spelling my.unimas.ir.65492023-07-03T06:55:25Z http://ir.unimas.my/id/eprint/6549/ User authentication using neural network in smart home Jee,, Tze Ling T Technology (General) 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. Conventional password-based authentication methods store passwords as a password or verification table which is vulnerable. In this project, a neural network is trained to store the passwords and replace verification table. This method is useful in solving security problems that happened in some authentication system. Furthermore, it can be applied to the door lock for a smart home 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 network to accelerate the training process. For the experiment, 200 sets of UserID and Passwords were created and encoded into binary as the input and target. The experiment had been carried out to evaluate the performance for different number of hidden neurons, training sets, and combination of transfer functions. Mean Square Error (MSE), training time and number of epochs are used to determine the network performance. From the simulation results obtained, using Tansig and Purelin in hidden and output layer, and 250 hidden neurons gave the better performance. The network which gives the better performance network is used to develop the user authentication system. Universiti Malaysia Sarawak, (UNIMAS) 2009 Final Year Project Report NonPeerReviewed text en http://ir.unimas.my/id/eprint/6549/1/User%20Authentication%20Using%20Neural%20Network%20in%20Smart%20Home%2824%20pgs%29.pdf text en http://ir.unimas.my/id/eprint/6549/8/User%20Authentication%20Using%20Neural%20Network%20in%20Smart%20Home%28OCR%29.pdf Jee,, Tze Ling (2009) User authentication using neural network in smart home. [Final Year Project Report] (Unpublished)
institution Universiti Malaysia Sarawak
building Centre for Academic Information Services (CAIS)
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sarawak
content_source UNIMAS Institutional Repository
url_provider http://ir.unimas.my/
language English
English
topic T Technology (General)
spellingShingle T Technology (General)
Jee,, Tze Ling
User authentication using neural network in smart home
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. Conventional password-based authentication methods store passwords as a password or verification table which is vulnerable. In this project, a neural network is trained to store the passwords and replace verification table. This method is useful in solving security problems that happened in some authentication system. Furthermore, it can be applied to the door lock for a smart home 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 network to accelerate the training process. For the experiment, 200 sets of UserID and Passwords were created and encoded into binary as the input and target. The experiment had been carried out to evaluate the performance for different number of hidden neurons, training sets, and combination of transfer functions. Mean Square Error (MSE), training time and number of epochs are used to determine the network performance. From the simulation results obtained, using Tansig and Purelin in hidden and output layer, and 250 hidden neurons gave the better performance. The network which gives the better performance network is used to develop the user authentication system.
format Final Year Project Report
author Jee,, Tze Ling
author_facet Jee,, Tze Ling
author_sort Jee,, Tze Ling
title User authentication using neural network in smart home
title_short User authentication using neural network in smart home
title_full User authentication using neural network in smart home
title_fullStr User authentication using neural network in smart home
title_full_unstemmed User authentication using neural network in smart home
title_sort user authentication using neural network in smart home
publisher Universiti Malaysia Sarawak, (UNIMAS)
publishDate 2009
url http://ir.unimas.my/id/eprint/6549/1/User%20Authentication%20Using%20Neural%20Network%20in%20Smart%20Home%2824%20pgs%29.pdf
http://ir.unimas.my/id/eprint/6549/8/User%20Authentication%20Using%20Neural%20Network%20in%20Smart%20Home%28OCR%29.pdf
http://ir.unimas.my/id/eprint/6549/
_version_ 1770555380587298816
score 13.214268