Hybrid model with fusion approach to enhance the efficiency of keystroke dynamics authentication

Abstract

We propose in this paper a novel technique to enhance the performance of keystroke dynamic authentication using hybrid model with four fusion approach. Firstly, extract keystroke features from our database. Then generate template from extracted features, which is compact form of keystroke feature data. Hybrid model based on combination of Gaussian probability density function (GPDF) and Support Vector Machine (SVM) will convert test features into scores. At last, applied four fusion rules on hybrid model to fusing GPDF and SVM scores to improve the final result. Experimental results show that the performance of the proposed hybrid model can bring obvious improvement with error rate of 1.612%.