Thesis/Project Final Defense Schedule
Join us as the School of STEM master’s degree candidates present their culminating thesis and project work. Check back mid quarter for any new quarter defenses.
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Select a master’s program to navigate to candidates:
Master of Science in Computer Science & Software Engineering
AUTUMN 2023
Thursday, November 30
Arun Sarma
Chair: Dr. Geethapriya Thamilarasu
Candidate: Master of Science in Computer Science & Software Engineering
11:00 A.M.; Discovery Hall 464
Project: Biosignal Based Side Channel Attack to Infer Android Pattern Lock Using Deep Learning
The growing popularity of wearable Internet of Things (IoT) devices has led to significant security and privacy concerns. The health data that these devices collect can be used to infer private and sensitive user information via side channel attacks. This is especially true for users of Brain-Computer Interfaces (BCI) which measures brain activity via Electroencephalography (EEG) signals, and sometimes muscle movement from Electromyography (EMG) signals in Human-Computer Interaction (HCI) applications. Studies show attacks have been constructed to infer various sensitive information such as PINs and passwords from BCI users’ biosignal data. However, to our knowledge, no side channel attacks have been demonstrated on popular, alternative authentication methods such as Android Pattern Lock. Existing research shows that Android Pattern Lock can be cracked using video-based and acoustic side channel attacks. However, these attacks require direct observation of the victim or access to the victim’s smartphone sensors which can be locked behind strict device permissions. Motivated by the vulnerabilities of consumer-grade IoT devices that record biosignal data, we propose a novel side channel attack where recorded EEG and EMG signals are analyzed using deep learning techniques to infer a victim’s Android Pattern Lock. Our experiment results show that our side channel attack detects when a user is unlocking their phone via Pattern Lock with a 98.97% accuracy and infers the drawn pattern with a 99.97% accuracy. General swipe directions of the user’s finger drawing the unlock pattern is inferred with a 93.64% accuracy.
Thursday, December 7
CHENGCHENG YANG
Chair: Dr. Annuska Zolyomi
Candidate: Master of Science in Computer Science & Software Engineering
8:45 A.M.; Join Chengcheng Yang’s online defense
Thesis: User Centered Re-design of Pediatric Online Portal
Clinicians, educators, and families need access to trustworthy information about developmental-behavioral pediatrics (DBP); however, credible online information is limited due to the scarcity of medical professionals specializing in this field. To address this issue, a pediatrician with extensive knowledge of DBP created an online portal specifically designed to provide clinicians, educators, and parents with access to DBP-related resources. This project focuses on redesigning the portal to effectively cater to the diverse needs and values of multiple stakeholders, each possessing unique knowledge and information requirements regarding developmental and behavioral disabilities. Through the implementation of user-centered design principles, the portal was enhanced to promote trust and alleviate anxiety, particularly among families with children facing developmental and behavioral challenges.
This project established guidelines for redesigning a health portal based on a comprehensive literature review and competitive analysis. Key design guidelines included conveying the organization’s ‘real-world’ aspect, ensuring clarity of purpose, user-friendly navigation, consistent layout and color scheme, enhanced visual appeal with bright images, effective use of fonts and colors for conveying information, and ensuring accessibility for dyslexia and color blindness. Additionally, a thorough understanding of user needs was gained by analyzing key stakeholders, developing personas, crafting scenarios, constructing journey maps, and employing information architecture techniques.
To continuously refine the design throughout the process, participatory design, interviews, and usability testing were employed to identify and address emerging design challenges and user requirements. Primary users shared common requirements for user-friendliness and intuitive navigation. However, parents placed greater emphasis on the search bar function, as they were not familiar with DBP terminology and sought a quick and efficient way of locating relevant information. Additionally, parents expressed a strong preference for websites displaying the logo of a well-known hospital, as it instilled a sense of trust and credibility. By iteratively refining design solutions, the project improved the user experience, ultimately ensuring that the final design of the portal effectively met the needs of its real-world diverse user base in a trustworthy manner. The outstanding achievements, including a 300% reduction in search time and task incompleteness, and the significant improvements in System Usability Scale (SUS) scores for both primary users (174%) and the general public (226%), underscore the impactful and transformative nature of the user-centered design approach.
WEN-JUI CHENG
Chair: Dr. William Erdly
Candidate: Master of Science in Computer Science & Software Engineering
11:00 A.M.; Join Wen-Jui Cheng’s online defense
Thesis: Facilitating Physical Elements of Fine Arts Education Through Motion-Controlled Simulation
For traditional, in-person academia, students often face learning difficulties due to uncontrollable conditions in the real world. In particular, students who cannot attend classes in-person due to sickness, family or important obligations lose the opportunity to practice physical skills that cannot be experienced in remote settings, but are nevertheless essential to their field of study. An optimal solution should allow students to learn through physical interactions with class elements without being present in a classroom setting. This study provides a starting point by developing a virtual, motion-controlled environment that facilitates the process of drawing or painting with various art tools. Using Virtual Reality (VR), we created a simulation that enabled users to draw or paint using seven unique tools and ten different colors. By measuring our simulation’s usability and immersion through five central metrics (Effectiveness, Efficiency, Satisfaction, Fidelity and Quality), we discovered that our virtual environment provided an adequate user experience as a software, but lacked accuracy to real-world elements. Given more time in the future, we hope to enhance the immersion and intuition of our simulation to seamlessly translate virtual experiences to the real world.
JESSE LEU
Chair: Dr. Erika Parsons
Candidate: Master of Science in Computer Science & Software Engineering
5:45 P.M.; Discovery Hall 464
Project: Exploring Data Preprocessing and Statistical Analysis Strategies for Intracranial Hemorrhage Detection Based on Ultrasound Tissue Pulsatility Imaging
Traumatic Brain Injury (TBI) is a serious health concern, impacting brain function with potential consequences ranging from temporary challenges to severe, life-threatening intracranial hemorrhages. Timely detection is crucial for ensuring prompt and targeted care that leads to improved patient outcomes. While conventional diagnostic methods such as Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) have constraints such as limited portability, high costs and the need for skilled technicians to gather the data, the investigation into Ultrasound Imaging, specifically Tissue Pulsatility Imaging (TPI), offers a viable alternative as it overcomes the aforementioned issues. However, unlike CT and MRI techniques that capture static images (essentially “snapshots” of tissues), Ultrasound technology gathers a continuous series of measurements over time, akin to a movie with multiple frames. Subsequently, these dynamic measurements must undergo processing to simulate static images resembling those obtained through CT or MRI. Hence, the data collected through ultrasound remains challenging to process and interpret; specifically, it poses difficulties for immediate utilization by Machine Learning (ML) strategies and data analysis methods such as component analysis. Consequently, additional preprocessing steps are essential to extract aspects from this data relevant to our work.
This project focuses on analyzing data collected from experiments involving patients that have suffered TBI, leveraging TPI to examine brain and tissue displacements across cardiac cycles. The primary goal is to study tissue displacement patterns in both, healthy and injured brains to try to find features and metrics that can help us differentiate between them using ML and data analysis techniques. It is of particular interest to look at TBI patients that have suffered critical bleeding. The overarching objective of this project is to enhance and automate existing methodologies and consists of two main phases:
In the initial phase, a comprehensive study of the data processing pipeline was conducted to identify bottlenecks and optimization opportunities. Given the multidisciplinary nature of the project, the data is intricate, requiring substantial effort and external knowledge for relevant content extraction. Notably, the project automated the process of downloading data from the cloud drive, organized it into folders by patients, and generated representative data for further analysis. In the subsequent phase, we explored the potential of identifying and analyzing displacement metrics to enhance intracranial hemorrhage detection. This involved the use of statistics, data visualization, and other techniques such as Component Analyses, and ML spatial models. Furthermore, we extended previous research by exploring how the identification of certain displacement values (such as minimum and maximum) can contribute to the identification and differentiation of intracranial hemorrhage.
During the course of this project, we have identified opportunities and limitations for optimizing the data preprocessing pipeline, mainly related to the dependencies and structure of the collected data. From a data analysis perspective, as we built upon prior research, we observed that the peak displacement values can be utilized to distinguish between TBI and healthy patient data. Some of such findings lay the groundwork for further investigation and refinement of ML models to enhance the accuracy of intracranial hemorrhage detection.
Master of Science in Cybersecurity Engineering
Autumn 2023
Friday, December 8
NEIL PRAKASAM
Chair: Dr. Bren Lagesse
Candidate: Master of Science in Computer Science & Software Engineering
11:00 A.M.; Join Neil Prakasam’s online defense
Thesis: A System for Secure and Categorized Video-Sharing
Online video sharing is a phenomenon which continues to be increasingly utilized by the entire population. Preserving the privacy of videos shared online is of utmost importance, but there is one use case that hasn’t yet been covered by current mainstream video sharing platforms. This project aims to provide the ability to categorize whether multiple videos are of the same event, so that users can share them only amongst others who were also present at the event and have video evidence. The main method of categorization will be through DNA sequencing, where video files will be converted into literal dna in order to be categorized into 4 categories. This includes those that are of the same event, space, activity, or are completely different videos. The research has shown rather lackluster results that could potentially be further optimized to categorize videos between the 4 categories, let alone whether or not they are of the same event. This paper will introduce and implement multiple methods of doing so.
JAMES EARRON COOPER
Chair: Dr. Brent Lagesse
Candidate: Master of Science in Cybersecurity Engineering
1:15 P.M.; UW1-361
Project: Peer Validated Proof-of-Presence for Crowdsensing Applications and Other Location Critical Apps/Services
An issue prevalent in crowdsourcing applications, but applicable to many other types of apps and services, is the challenge of authenticating a user’s location; and issues sometimes referred to as proof-of-presence. A notable concern in crowdsourcing apps is the submission of data by malicious users that was not legitimately collected in order to collect incentives that are being offered. Relying solely on GPS for location determination can be risky given the susceptibility of smartphones to GPS spoofing. Alternative methods, such as trilateration of cellular signals and databases of pre-mapped Wi-Fi access points, exist but often provide low accuracy.
This project attempts to addresses the proof-of-presence issue by comparing contextual data collected about the environment from all users to determine the ground truth and identify potential outliers. The system utilizes the user location and data about detected Wi-Fi signals to construct a dynamic “map” of WiFi access points and employs a variety of probabilistic techniques to assign each user a “score” indicating the likelihood of the inputs being potentially erroneous or fabricated. While the system achieves its goals under simplified and ideal laboratory conditions, real-world scenarios pose significant challenges. This project lays the foundation for a more robust and complicated system capable of addressing those challenges.