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A multilayer neural network system for computer access security

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2 Author(s)
Obaidat, M.S. ; Dept. of Electr. Eng., City Coll. of New York, NY, USA ; Macchairolo, D.T.

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

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Systems, Man and Cybernetics, IEEE Transactions on  (Volume:24 ,  Issue: 5 )