A NONPARAMETRIC BAYESIAN FRAMEWORK FOR MOBILE DEVICE SECURITY AND LOCATION BASED SERVICES

In June 2013, it was reported that, for the first time, more than half of American adults have smartphones [1]. Smartphones are carried by the users most of the time and used to access all types of personal sensitive information, from email, Facebook to banking, files server, etc. The fact that humans are more and more attached to their phones poses both good and bad aspects, namely, security challenges and new opportunities in improving users experiences. From the bad aspect, it is of interest to find out how to protect the users from cyber attack. Whereas from the good aspect, we can improve users’ experiences by providing services related to their locations.

To address the first problem, in this dissertation, we propose a security framework to detect two kinds of attacks, the Masquerade attack and the Sybil attack. Most existing literature employs supervised learning and assumes the number of devices is known. We, on the other hand, propose a non-parametric Bayesian method to detect the number of devices as well as determine which observations belong to which devices in an unsupervised passive manner. An attack can be detected by comparing the number of registered users with the number of devices found, and the malicious nodes are found based on the labels of their observations.

For the second problem, we propose a location based service (LBS) enabler framework by providing a high accuracy indoor location identification and future location prediction algorithms. LBS are applications in which, locations of users are utilized to activate a set of services which significantly improve users experiences. Examples include a micro-climate control application, which can automatically adjust room temperature given that the room is occupied. It also can be a network scheduling users’ access application, where users’ future whereabouts can be predicted and used for arranging files transfer to better enhance users’ experiences.

In this dissertation, we mainly focus our research on the above two fields. The nonparametric Bayesian framework was used as the generative model for both the observations extracted from the wireless signal in the wireless security problem, as well the observations extracted from the features that represent a location in LBS. Beside the framework, the major contributions of the dissertation include a missing data handling algorithm, a light-weight indoor place identification algorithm, a stopping rule to terminate the algorithm in a quickest way while maintaining a acceptable false alarm rate, and a passive approach to defend against Masquerade and Sybil attacks in wireless networks. Moreover, several mechanisms to predict users’ future whereabouts such as a Dynamic Hidden Markov Model that can evolve itself over time, or a prediction model based on Deep Learning were proposed. Most of the algorithms are evaluated using experimental data and proved to obtain considerably high performances compared with other state-of-the-art approaches.