Application of Neural Network in User Authentication for Smart Home System

Authors: A. Joseph, D.B.L. Bong, D.A.A. Mat

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

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1072391

References:

[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.