ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
In this dissertation, I am interested in the special authentication demands of smart devices and about to satisfy the demands. First, I study how the features of smart devices affect user identity authentications. For identity authentication domain, I aim to design a continuous, forge-resistant authentication mechanism that does not interrupt user-device interactions. I propose a mechanism that authenticates user identity based on the user's finger movement patterns. Next, I study a smart-device-specific authentication, proximity authentication, which authenticates whether two devices are in close proximity. For prox- imity authentication domain, I aim to design a user-friendly authentication mechanism that can defend against relay attacks. In addition, I restrict the authenticated distance to the scale of near field, i.e., a few centimeters. My first design utilizes a user's coherent two-finger movement on smart device screen to restrict the distance. To achieve a fully-automated system, I explore acoustic communications and propose a novel near field authentication system.
Modification and addition of rules without knowledge of previous rules creates anomalies like shadowing and rule redundancy. Anomalous rule sets not only limit the efficiency of the firewall but in some cases create a hole in the perimeter security. Detection of anomalies has been studied for a long time and some well established procedures have been implemented and tested. But they all have a common problem of visualizing the results. When it comes to visualization of firewall anomalies, the results do not fit in traditional matrix, tree or sunburst representations.
This research targets the anomaly detection and visualization problem. It analyzes and represents firewall rule anomalies in innovative ways such as hive plots and dynamic slices. Such graphical representations of rule anomalies are useful in understanding the state of a firewall. It also helps network administrators in finding and fixing the anomalous rules.
MFI zeolite membranes were synthesized on α-alumina supports by secondary growth method. Novel positron annihilation spectroscopy (PAS) techniques were used to non-destructively characterize the pore structure of these membranes. PAS reveals a bimodal pore structure consisting of intracrystalline zeolitic micropores of ~0.6 nm in diameter and irregular intercrystalline micropores of 1.4 to 1.8 nm in size for the membranes. The template-free synthesized membrane exhibited a high permeance but a low selectivity in C3H6/C3H8 mixture separation.
CMS membranes were synthesized by coating/pyrolysis method on mesoporous γ-alumina support. Such supports allow coating of thin, high-quality polymer films and subsequent CMS membranes with no infiltration into support pores. The CMS membranes show strong molecular sieving effect, offering a high C3H6/C3H8 mixture selectivity of ~30. Reduction in membrane thickness from 500 nm to 300 nm causes an increase in C3H8 permeance and He/N2 selectivity, but a decrease in the permeance of He, N2 and C3H6 and C3H6/C3H8 selectivity. This can be explained by the thickness dependent chain mobility of the polymer film resulting in final carbon membrane of reduced pore size with different effects on transport of gas of different sizes, including possible closure of C3H6-accessible micropores.
CMS membranes demonstrate excellent C3H6/C3H8 separation performance over a wide range of feed pressure, composition and operation temperature. No plasticization was observed at a feed pressure up to 100 psi. The permeation and separation is mainly controlled by diffusion instead of adsorption. CMS membrane experienced a decline in permeance, and an increase in selectivity over time under on-stream C3H6/C3H8 separation. This aging behavior is due to the reduction in effective pore size and porosity caused by oxygen chemisorption and physical aging of the membrane structure.
A user vulnerability can be managed in three steps: (1) identifying, (2) measuring and (3) reducing a user vulnerability. Researchers have long been identifying vulnerabilities arising from user's personal data, including user names, demographic attributes, lists of friends, wall posts and associated interactions, multimedia data such as photos, audios and videos, and tagging of friends. Hence, this research first proposes a way to measure and reduce a user vulnerability to protect such personal data. This dissertation also proposes an algorithm to minimize a user's vulnerability while maximizing their social utility values.
To address these vulnerability concerns, social networking sites like Facebook usually let their users to adjust their profile settings so as to make some of their data invisible. However, users sometimes interact with others using unprotected posts (e.g., posts from a ``Facebook page\footnote{The term ''Facebook page`` refers to the page which are commonly dedicated for businesses, brands and organizations to share their stories and connect with people.}''). Such interactions help users to become more social and are publicly accessible to everyone. Thus, visibilities of these interactions are beyond the control of their profile settings. I explore such unprotected interactions so that users' are well aware of these new vulnerabilities and adopt measures to mitigate them further. In particular, {\em are users' personal attributes predictable using only the unprotected interactions}? To answer this question, I address a novel problem of predictability of users' personal attributes with unprotected interactions. The extreme sparsity patterns in users' unprotected interactions pose a serious challenge. Therefore, I approach to mitigating the data sparsity challenge by designing a novel attribute prediction framework using only the unprotected interactions. Experimental results on Facebook dataset demonstrates that the proposed framework can predict users' personal attributes.