This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students. 

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Recent advances in cyber-physical systems, artificial intelligence, and cloud computing have driven the widespread deployment of Internet-of-Things (IoT) devices in smart homes. However, the spate of cyber attacks exploiting the vulnerabilities and weak security management of smart home IoT devices have highlighted the urgency and challenges of designing efficient mechanisms

Recent advances in cyber-physical systems, artificial intelligence, and cloud computing have driven the widespread deployment of Internet-of-Things (IoT) devices in smart homes. However, the spate of cyber attacks exploiting the vulnerabilities and weak security management of smart home IoT devices have highlighted the urgency and challenges of designing efficient mechanisms for detecting, analyzing, and mitigating security threats towards them. In this dissertation, I seek to address the security and privacy issues of smart home IoT devices from the perspectives of traffic measurement, pattern recognition, and security applications. I first propose an efficient multidimensional smart home network traffic measurement framework, which enables me to deeply understand the smart home IoT ecosystem and detect various vulnerabilities and flaws. I further design intelligent schemes to efficiently extract security-related IoT device event and user activity patterns from the encrypted smart home network traffic. Based on the knowledge of how smart home operates, different systems for securing smart home networks are proposed and implemented, including abnormal network traffic detection across multiple IoT networking protocol layers, smart home safety monitoring with extracted spatial information about IoT device events, and system-level IoT vulnerability analysis and network hardening.
ContributorsWan, Yinxin (Author) / Xue, Guoliang (Thesis advisor) / Xu, Kuai (Thesis advisor) / Yang, Yezhou (Committee member) / Zhang, Yanchao (Committee member) / Arizona State University (Publisher)
Created2023
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Description
Antibiotic resistance is a very important issue that threatens mankind. As bacteria

are becoming resistant to multiple antibiotics, many common antibiotics will soon

become ineective. The ineciency of current methods for diagnostics is an important

cause of antibiotic resistance, since due to their relative slowness, treatment plans

are often based on physician's experience rather

Antibiotic resistance is a very important issue that threatens mankind. As bacteria

are becoming resistant to multiple antibiotics, many common antibiotics will soon

become ineective. The ineciency of current methods for diagnostics is an important

cause of antibiotic resistance, since due to their relative slowness, treatment plans

are often based on physician's experience rather than on test results, having a high

chance of being inaccurate or not optimal. This leads to a need of faster, pointof-

care (POC) methods, which can provide results in a few hours. Motivated by

recent advances on computer vision methods, three projects have been developed

for bacteria identication and antibiotic susceptibility tests (AST), with the goal of

speeding up the diagnostics process. The rst two projects focus on obtaining features

from optical microscopy such as bacteria shape and motion patterns to distinguish

active and inactive cells. The results show their potential as novel methods for AST,

being able to obtain results within a window of 30 min to 3 hours, a much faster

time frame than the gold standard approach based on cell culture, which takes at

least half a day to be completed. The last project focus on the identication task,

combining large volume light scattering microscopy (LVM) and deep learning to

distinguish bacteria from urine particles. The developed setup is suitable for pointof-

care applications, as a large volume can be viewed at a time, avoiding the need

for cell culturing or enrichment. This is a signicant gain compared to cell culturing

methods. The accuracy performance of the deep learning system is higher than chance

and outperforms a traditional machine learning system by up to 20%.
ContributorsIriya, Rafael (Author) / Turaga, Pavan (Thesis advisor) / Wang, Shaopeng (Committee member) / Grys, Thomas (Committee member) / Zhang, Yanchao (Committee member) / Arizona State University (Publisher)
Created2020