With the progress achieved to this date in mobile computing technologies, mobile devices are increasingly being used to store sensitive data and perform security-critical transactions and services. However, the protection available on these devices is still lagging behind. The primary and often only protection mechanism in these devices is authentication using a password or a PIN. Passwords are notoriously known to be a weak authentication mechanism, no matter how complex the underlying format is. Mobile authentication can be strengthened by extracting and analyzing keystroke dynamic biometric from supplied passwords. In this thesis, I identified gaps in the literature, and investigated new models and mechanisms to improve accuracy, usability and resilience against statistical forgeries for mobile keystroke dynamic biometric authentication. Accuracy is investigated through cost sensitive learning and sampling, and by comparing the strength of different classifiers. Usability is improved by introducing a new approach for typo handling in the authentication model. Resilience against statistical attacks is achieved by introducing a new multimodal approach combining fixed and variable keystroke dynamic biometric passwords, in which two different fusion models are studied. Experimental evaluation using several datasets, some publicly available and others collected locally, yielded encouraging performance results in terms of accuracy, usability, and resistance against statistical attacks. / Graduate / 2019-09-25