Trajectory Mining for Keystroke Dynamics Authentication

Abstract

This paper focuses on enhancing a username and password authentication scheme, which has some weaknesses because a username is publicly known and a password can be guessed. When an attacker knows or guesses a password correctly, the system is compromised. Therefore, this research focuses on this weakness and proposes an additional security token to this scheme by combining keystroke dynamics into the system. A username is typically not changed but a password is required to change frequently for a better security level. A username is typed frequently such that the familiar typing can be used as a behavioral biometrics of a user. Therefore, a keystroke dynamics profile is proposed using a trajectory dissimilarity technique to verify user's typing behavior on a username as an additional authentication token. Several features are mined for keystroke dynamics to create a trajectory profile, which gives the best results of 4% equal error rate (EER) or 96% authentication accuracy.