Machine Learning Algorithm on Keystroke Dynamics Pattern

Abstract

In this paper, the machine learning algorithms have been applied on distinct features of Keystroke Dynamics. The Machine learning is important to correctly authenticate an individual. In this work, the complex models and algorithms help to determine when the person is a genuine user or an imposter through learning. The algorithms that has been studied and deployed,are the Fuzzy Expert System (FESs), NeuroEvolution of the augmenting topology (NEAT), Proposed NeuroEvolution of the augmenting topology, Support Vector Machine (SVM) and Chaotic Neural Network. From the algorithms applied, the proposed NEAT algorithms performs better in terms of recognition rate on both databases used where the recognition rate achieved above 95.6%.