Comparison of Feature Vectors in Keystroke Dynamics: A Novelty Detection Approach

Abstract

A number of current applications require algorithms able to extract a model from one-class data and classify unseen data as self or non-self in a novelty detection scenario, such as spam identification and intrusion detection. In this paper the authors focus on keystroke dynamics, which analyses the user typing rhythm to improve the reliability of user authentication process. However, several different features may be extracted from the typing data, making it difficult to define the feature vector. This problem is even more critical in a novelty detection scenario, when data from the negative class is not available. Based on a keystroke dynamics review, this work evaluated the most used features and evaluated which ones are more significant to differentiate a user from another using keystroke dynamics. In order to perform this evaluation, the authors tested the impact on two benchmark databases applying bio-inspired algorithms based on neural networks and artificial immune systems.