Please note: This master’s thesis presentation will be given online.
Ezzeldin Tahoun, Master’s candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Urs Hengartner
Authenticating a user’s identity lies at the heart of securing any information system. A trade-off exists currently between user experience and the level of security the system abides by. Using Continuous and Implicit Authentication a user’s identity can be verified without any active participation, hence increasing the level of security, given the continuous verification aspect, as well as the user experience, given its implicit nature.
This thesis studies using mobile devices’ inertial sensors data to identify unique movements and patterns that identify the owner of the device at all times. We implement and evaluate approaches proposed in related works as well as novel approaches based on a variety of machine learning models, specifically a new kind of Auto Encoder (AE) named Variational Auto Encoder (VAE), relating to the generative models family. We evaluate numerous machine learning models for the anomaly detection or outlier detection case of spotting a malicious user, or an unauthorized entity currently using the smartphone system. We evaluate the results under conditions similar to other works as well as under conditions typically observed in real-world applications. We find that the shallow VAE is the best performer semi-supervised anomaly detector in our evaluations and hence the most suitable for the design proposed.
The thesis concludes with recommendations for the enhancement of the system and the research body dedicated to the domain of Continuous and Implicit Authentication for mobile security.
Keywords: Machine Learning, Generative Models, Continuous Authentication, Implicit Authentication, Artificial Intelligence
To join this master’s thesis presentation on MS Teams, please go to https://tinyurl.com/ezz-thesis.
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