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 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.
[1] I.C.Lin,H.H. Ou, M.S. Hwang, "A user Authentication System using
Back-propagation Network," Neural Comput & Applic, June 2005
[2] U. Manber, "A Simple Scheme to make Passwords Based on One Way
Functions Much Harder to Crack",Nov 2000
[3] S.Z. Reyhani, M. Mahdavi, "User Authetication Using Neural Network
in Smart Home Networks," International Journal of Smart Home, Vol 1
no 2,pp147, July 2007.
[4] H. Demuth, M.Beale, M.Hagan, " Neural Network ToolboxTM User
Guide: Faster Training," Natick: The MathworksTM Inc, 2008.
[5] M. Curphey, A Guide to Building Secure Web Application, The Open
Web Application Security Project (OWASP), Boston, USA, 2002..
[6] A. Pavelka, A.Proch- azka, " Algorithm for Initialization of Neural
Network weights, Institute of Chemical Technology, department of
Computing and Control Engineering.
[1] I.C.Lin,H.H. Ou, M.S. Hwang, "A user Authentication System using
Back-propagation Network," Neural Comput & Applic, June 2005
[2] U. Manber, "A Simple Scheme to make Passwords Based on One Way
Functions Much Harder to Crack",Nov 2000
[3] S.Z. Reyhani, M. Mahdavi, "User Authetication Using Neural Network
in Smart Home Networks," International Journal of Smart Home, Vol 1
no 2,pp147, July 2007.
[4] H. Demuth, M.Beale, M.Hagan, " Neural Network ToolboxTM User
Guide: Faster Training," Natick: The MathworksTM Inc, 2008.
[5] M. Curphey, A Guide to Building Secure Web Application, The Open
Web Application Security Project (OWASP), Boston, USA, 2002..
[6] A. Pavelka, A.Proch- azka, " Algorithm for Initialization of Neural
Network weights, Institute of Chemical Technology, department of
Computing and Control Engineering.
@article{"International Journal of Information, Control and Computer Sciences:58311", author = "A. Joseph and D.B.L. Bong and D.A.A. Mat", title = "Application of Neural Network in User Authentication for Smart Home System", abstract = "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.", keywords = "Neural Network, User Authentication, Smart Home,
Security", volume = "3", number = "5", pages = "1360-8", }