ETDs

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.

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Mining IoT Network Traffic in Smart Homes: Traffic Measurement, Pattern Recognition, and Security Applications

Description
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