Competition between SOM Clusters to Model User Authentication System in Computer Networks

Abstract

Traditional authentication systems employed on Internet are facing an acute problem of intrusions. In this context we propose a neural architecture for user authentication through keystroke dynamics. Proposed architecture consists of a set of self organizing maps where each user has a distinct map. Each map consists of n neurons in the input layer where n is the length of a keystroke pattern; however to determine the number of neurons in the output layer, a strategy is proposed. For authenticating claimed user, probable user(s) for a given pattern and the degree of similarity between the map of the claimed user and a given pattern are determined. Finally, a decision on the authenticity is made using threshold criteria. Evaluation results show the best false accept rate of 0.88% when false reject rate was 3.55% with authentication accuracy of 97.83%. An application scenario of the method in a computer network environment is also presented.