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

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.

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