Title :
A multilayer neural network system for computer access security
Author :
Obaidat, M.S. ; Macchairolo, D.T.
Author_Institution :
Dept. of Electr. Eng., City Coll. of New York, NY, USA
fDate :
5/1/1994 12:00:00 AM
Abstract :
This paper presents a new multilayer neural network system to identify computer users. The input vectors were made up of the time intervals between successive keystrokes created by users while typing a known sequence of characters. Each input vector was classified into one of several classes, thereby identifying the user who typed the character sequence. Three types of networks were discussed: a multilayer feedforward network trained using the backpropagation algorithm, a sum-of-products network trained with a modification of backpropagation, and a new hybrid architecture that combines the two. A maximum classification accuracy of 97.5% was achieved using a neural network based pattern classifier. Such approach can improve computer access security
Keywords :
authorisation; backpropagation; biometrics (access control); feedforward neural nets; backpropagation algorithm; classification accuracy; computer access security; computer users; feedforward network; hybrid architecture; multilayer neural network system; neural network based pattern classifier; sum-of-products network; Application software; Artificial neural networks; Back; Computer networks; Computer security; Decision support systems; Multi-layer neural network; Neural networks; Optical signal processing; Pattern recognition;
Journal_Title :
Systems, Man and Cybernetics, IEEE Transactions on