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Behavioural biometric identification based on human computer interaction

Hinbarji, Zaher (2018) Behavioural biometric identification based on human computer interaction. PhD thesis, Dublin City University.

Abstract
As we become increasingly dependent on information systems, personal identification and profiling systems have received an increasing interest, either for reasons of personali- sation 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 cre- ation of user profiles to be applied in dynamic user identification based on the biometric pat- terns 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 sys- tems 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.
Metadata
Item Type:Thesis (PhD)
Date of Award:January 2018
Refereed:No
Supervisor(s):Gurrin, Cathal and Albatal, Rami
Uncontrolled Keywords:lifelogging; biometrics; behavioural boimetrics
Subjects:Computer Science > Lifelog
Computer Science > Machine learning
Computer Science > Artificial intelligence
Computer Science > Multimedia systems
Computer Science > Information retrieval
DCU Faculties and Centres:Research Initiatives and Centres > INSIGHT Centre for Data Analytics
DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 3.0 License. View License
Funders:Science Foundation Ireland
ID Code:22187
Deposited On:05 Apr 2018 09:48 by Cathal Gurrin . Last Modified 19 Jul 2018 15:12
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