Theses and dissertations

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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Understanding User Privacy Issues: Publishing User Data with Privacy in Mind

Description
The explosive Web growth in the last decade has drastically changed the way billions of people all around the globe conduct numerous activities including creating, sharing, and consuming information. The massive amount of user-generated information encourages companies and service providers to collect users' information and use it in order to…

The explosive Web growth in the last decade has drastically changed the way billions of people all around the globe conduct numerous activities including creating, sharing, and consuming information. The massive amount of user-generated information encourages companies and service providers to collect users' information and use it in order to better their own goals and then further provide personalized services to users as well. However, the users' information contains their private and sensitive information and can lead to breach of users' privacy. Anonymizing users' information before publishing and using such data is vital in securing their privacy. Due to the many forms of user information (e.g., structural, interactions, etc), different techniques are required for anonymization of users' data. In this thesis, first we discuss different anonymization techniques for various types of user-generated data, i.e., network graphs, web browsing history, and user-item interactions. Our experimental results show the effectiveness of such techniques for data anonymization. Then, we briefly touch on securely and privately sharing information through blockchains.
ContributorsNou, Alex Sheavin (Author) / Liu, Huan (Thesis director) / Beigi, Ghazaleh (Committee member) / Computer Science and Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
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Categorization of Phishing Detection Features And Using the Feature Vectors to Classify Phishing Websites

Description
Phishing is a form of online fraud where a spoofed website tries to gain access to user's sensitive information by tricking the user into believing that it is a benign website. There are several solutions to detect phishing attacks such as educating users, using blacklists or extracting phishing characteristics found…

Phishing is a form of online fraud where a spoofed website tries to gain access to user's sensitive information by tricking the user into believing that it is a benign website. There are several solutions to detect phishing attacks such as educating users, using blacklists or extracting phishing characteristics found to exist in phishing attacks. In this thesis, we analyze approaches that extract features from phishing websites and train classification models with extracted feature set to classify phishing websites. We create an exhaustive list of all features used in these approaches and categorize them into 6 broader categories and 33 finer categories. We extract 59 features from the URL, URL redirects, hosting domain (WHOIS and DNS records) and popularity of the website and analyze their robustness in classifying a phishing website. Our emphasis is on determining the predictive performance of robust features. We evaluate the classification accuracy when using the entire feature set and when URL features or site popularity features are excluded from the feature set and show how our approach can be used to effectively predict specific types of phishing attacks such as shortened URLs and randomized URLs. Using both decision table classifiers and neural network classifiers, our results indicate that robust features seem to have enough predictive power to be used in practice.
ContributorsNamasivayam, Bhuvana Lalitha (Author) / Bazzi, Rida (Thesis advisor) / Zhao, Ziming (Committee member) / Liu, Huan (Committee member) / Arizona State University (Publisher)
Created2017