Date of Award
Sherif Saad Ahmed
adaptive algorithms, anomaly detection, biometrics, keystroke biometrics, machine learning
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
In this thesis, we argue that most of the work in the literature on behavioural-based biometric systems using AI and machine learning is immature and unreliable. Our analysis and experimental results show that designing reliable behavioural-based biometric systems requires a systematic and complicated process. We ﬁrst discuss the limitation in existing work and the use of conventional machine learning methods. We use the biometric zoos theory to demonstrate the challenge of designing reliable behavioural-based biometric systems. Then, we outline the common problems in engineering reliable biometric systems. In particular, we focus on the need for novelty detection machine learning models and adaptive machine learning algorithms. We provide a systematic approach to design and build reliable behavioural-based biometric systems. In our study, we apply the proposed approach to keystroke dynamics. Keystroke dynamics is behavioural-based biometric that identify individuals by measuring their unique typing behaviours on physical or soft keyboards. Our study shows that it is possible to design reliable behavioral-based biometrics and address the gaps in the literature.
Shah, Anjali Parag, "Towards Engineering Reliable Keystroke Biometrics Systems" (2020). Electronic Theses and Dissertations. 8421.
Available for download on Thursday, July 29, 2021