Keystroke user recognition through extreme learning machine and evolving cluster method

User Identification and User Verification are the primary problems in the area of Keystroke Dynamics. In the last decade there has been massive research in User Verification, and lesser research in User Identification. Both approaches take a username and a passphrase as input. In this paper, we introduce this problem of replacing authentication systems with the passphrase alone. This is done by using neural network based approach i.e. Extreme Learning Machine. ELM is a fast Single hidden layer feed forward network (SLFN) with good generalization performance. However the hidden layer in ELM does not have to be tuned. As an evolutionary step, we use a clustering based Semi-supervised approach (ECM-ELM) to User Recognition to combat variance in the accuracy of traditional ELMs. This research aims not only to address User Recognition problem but also to remove the instability in the accuracy of ELM. As per our simulation, ECM-ELM achieved a stable accuracy of 87% with the CMU Keystroke Dataset, while ELM achieved an unstable average accuracy of 90%.