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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.


Many models of colonial interaction are build from cases of European colonialism among Native American and African peoples, and, as a result, they are often ill-suited to account for state expansion and decline in non-Western contexts. This dissertation investigates social organization and intraregional interaction in a non-western colonial context to broaden understanding of colonial interaction in diverse sociocultural settings. Drawing on social identity theory, population genetics, and social network analysis, patterns of social organization at the margins of the expansive pre-Hispanic Tiwanaku state (ca. AD 500-1100) are examined. According to the dual diaspora model of Tiwanaku colonial organization in the …

Contributors
Johnson, Kent M., Buikstra, Jane E, Stojanowski, Christopher M, et al.
Created Date
2016

Many existing applications of machine learning (ML) to cybersecurity are focused on detecting malicious activity already present in an enterprise. However, recent high-profile cyberattacks proved that certain threats could have been avoided. The speed of contemporary attacks along with the high costs of remediation incentivizes avoidance over response. Yet, avoidance implies the ability to predict - a notoriously difficult task due to high rates of false positives, difficulty in finding data that is indicative of future events, and the unexplainable results from machine learning algorithms. In this dissertation, these challenges are addressed by presenting three artificial intelligence (AI) approaches to …

Contributors
Almukaynizi, Mohammed, Shakarian, Paulo, Huang, Dijiang, et al.
Created Date
2019