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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Format: | Final Year Project Report |
Language: | English English |
Published: |
Universiti Malaysia Sarawak, (UNIMAS)
2009
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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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Summary: | 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. |
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