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 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