Author: Zahar Hinbarji
Supervisor(s) and Committee member(s): Cathal Gurrin and Rami Albatal (joint supervisors)
As we become increasingly dependent on information systems, personal identification and profiling systems have received an increasing interest, either for reasons of personalisation or security. Biometric profiling is one means of identification which can be achieved by analysing something the user is or does (e.g., a fingerprint, signature, face, voice). This Ph.D. research focuses on behavioural biometrics, a subset of biometrics that is concerned with the patterns of conscious or unconscious behaviour of a person, involving their style, preference, skills, knowledge, motor-skills in any domain. In this work I explore the creation of user profiles to be applied in dynamic user identification based on the biometric patterns observed during normal Human-Computer Interaction (HCI) by continuously logging and tracking the corresponding computer events. Unlike most of the biometrics systems that need special hardware devices (e.g. finger print reader), HCI-based identification systems can be implemented using regular input devices (mouse or keyboard) and they do not require the user to perform specific tasks to train the system. Specifically, three components are studied in-depth: mouse dynamics, keystrokes dynamics and GUI based user behaviour. In this work I will describe my research on HCI-based behavioural biometrics, discuss the features and models I proposed for each component along with the result of experiments. In addition, I will describe the methodology and datasets I gathered using my LoggerMan application that has been developed specifically to passively gather behavioural biometric data for evaluation. Results show that normal Human-Computer Interaction reveals behavioural information with discriminative power sufficient to be used for user modelling for identification purposes.