Application of Neural Network in User Authentication for Smart Home System

Joseph, A. and Bong, David B L and Mat, D.A.A. (2009) Application of Neural Network in User Authentication for Smart Home System. International Science Index, Computer and Information Engineering, 3 (5). ISSN 2409-0441

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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.

Item Type: E-Article
Uncontrolled Keywords: Neural Network, User Authentication, Smart Home, Security, research, Universiti Malaysia Sarawak, unimas, university, universiti, Borneo, Malaysia, Sarawak, Kuching, Samarahan, ipta, education
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Academic Faculties, Institutes and Centres > Faculty of Engineering
Depositing User: Karen Kornalius
Date Deposited: 27 Sep 2017 02:59
Last Modified: 27 Sep 2017 02:59
URI: http://ir.unimas.my/id/eprint/17797

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