| Abstract: | User authentication is a crucial requirement for cloud service providers to prove that the outsourced data and services are safe from imposters. Keystroke dynamics is a promising behavioral biometrics for strengthening user authentication, however, current keystroke based solutions designed for certain datasets, for example, a fixed length text typed on a traditional personal computer keyboard and their authentication performances were not acceptable for other input devices nor free length text. Moreover, they suffer from a high dimensional feature space that degrades the authentication accuracy and performance. In this paper, a keystroke dynamics based authentication system is proposed for cloud environments that is applicable to fixed and free text typed on traditional and touch screen keyboards. The proposed system utilizes different feature extraction methods, as a preprocessing step, to minimize the feature space dimensionality. Moreover, different fusion rules are evaluated to combine the different feature extraction methods so that a set of the most relevant features is chosen. Because of the huge number of users' samples, a clustering method is applied to the users' profile templates to reduce the verification time. The proposed system is applied to three different benchmark datasets using three different classifiers. Experimental results demonstrate the effectiveness and efficiency of the proposed system. Copyright © 2015 John Wiley & Sons, Ltd. |
