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- Member of: Theses and Dissertations
Description
Internet browsers are today capable of warning internet users of a potential phishing attack. Browsers identify these websites by referring to blacklists of reported phishing websites maintained by trusted organizations like Google, Phishtank etc. On identifying a Unified Resource Locator (URL) requested by a user as a reported phishing URL, browsers like Mozilla Firefox and Google Chrome display an 'active' warning message in an attempt to stop the user from making a potentially dangerous decision of visiting the website and sharing confidential information like username-password, credit card information, social security number etc.
However, these warnings are not always successful at safeguarding the user from a phishing attack. On several occasions, users ignore these warnings and 'click through' them, eventually landing at the potentially dangerous website and giving away confidential information. Failure to understand the warning, failure to differentiate different types of browser warnings, diminishing trust on browser warnings due to repeated encounter are some of the reasons that make users ignore these warnings. It is important to address these factors in order to eventually improve a user’s reaction to these warnings.
In this thesis, I propose a novel design to improve the effectiveness and reliability of phishing warning messages. This design utilizes the name of the target website that a fake website is mimicking, to display a simple, easy to understand and interactive warning message with the primary objective of keeping the user away from a potentially spoof website.
However, these warnings are not always successful at safeguarding the user from a phishing attack. On several occasions, users ignore these warnings and 'click through' them, eventually landing at the potentially dangerous website and giving away confidential information. Failure to understand the warning, failure to differentiate different types of browser warnings, diminishing trust on browser warnings due to repeated encounter are some of the reasons that make users ignore these warnings. It is important to address these factors in order to eventually improve a user’s reaction to these warnings.
In this thesis, I propose a novel design to improve the effectiveness and reliability of phishing warning messages. This design utilizes the name of the target website that a fake website is mimicking, to display a simple, easy to understand and interactive warning message with the primary objective of keeping the user away from a potentially spoof website.
ContributorsSharma, Satyabrata (Author) / Bazzi, Rida (Thesis advisor) / Walker, Erin (Committee member) / Gaffar, Ashraf (Committee member) / Arizona State University (Publisher)
Created2015
Description
This thesis addresses the problem of online schema updates where the goal is to be able to update relational database schemas without reducing the database system's availability. Unlike some other work in this area, this thesis presents an approach which is completely client-driven and does not require specialized database management systems (DBMS). Also, unlike other client-driven work, this approach provides support for a richer set of schema updates including vertical split (normalization), horizontal split, vertical and horizontal merge (union), difference and intersection. The update process automatically generates a runtime update client from a mapping between the old the new schemas. The solution has been validated by testing it on a relatively small database of around 300,000 records per table and less than 1 Gb, but with limited memory buffer size of 24 Mb. This thesis presents the study of the overhead of the update process as a function of the transaction rates and the batch size used to copy data from the old to the new schema. It shows that the overhead introduced is minimal for medium size applications and that the update can be achieved with no more than one minute of downtime.
ContributorsTyagi, Preetika (Author) / Bazzi, Rida (Thesis advisor) / Candan, Kasim S (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2011
Description
Broadcast Encryption is the task of cryptographically securing communication in a broadcast environment so that only a dynamically specified subset of subscribers, called the privileged subset, may decrypt the communication. In practical applications, it is desirable for a Broadcast Encryption Scheme (BES) to demonstrate resilience against attacks by colluding, unprivileged subscribers. Minimal Perfect Hash Families (PHFs) have been shown to provide a basis for the construction of memory-efficient t-resilient Key Pre-distribution Schemes (KPSs) from multiple instances of 1-resilient KPSs. Using this technique, the task of constructing a large t-resilient BES is reduced to finding a near-minimal PHF of appropriate parameters. While combinatorial and probabilistic constructions exist for minimal PHFs with certain parameters, the complexity of constructing them in general is currently unknown. This thesis introduces a new type of hash family, called a Scattering Hash Family (ScHF), which is designed to allow for the scalable and ingredient-independent design of memory-efficient BESs for large parameters, specifically resilience and total number of subscribers. A general BES construction using ScHFs is shown, which constructs t-resilient KPSs from other KPSs of any resilience ≤w≤t. In addition to demonstrating how ScHFs can be used to produce BESs , this thesis explores several ScHF construction techniques. The initial technique demonstrates a probabilistic, non-constructive proof of existence for ScHFs . This construction is then derandomized into a direct, polynomial time construction of near-minimal ScHFs using the method of conditional expectations. As an alternative approach to direct construction, representing ScHFs as a k-restriction problem allows for the indirect construction of ScHFs via randomized post-optimization. Using the methods defined, ScHFs are constructed and the parameters' effects on solution size are analyzed. For large strengths, constructive techniques lose significant performance, and as such, asymptotic analysis is performed using the non-constructive existential results. This work concludes with an analysis of the benefits and disadvantages of BESs based on the constructed ScHFs. Due to the novel nature of ScHFs, the results of this analysis are used as the foundation for an empirical comparison between ScHF-based and PHF-based BESs . The primary bases of comparison are construction efficiency, key material requirements, and message transmission overhead.
ContributorsO'Brien, Devon James (Author) / Colbourn, Charles J (Thesis advisor) / Bazzi, Rida (Committee member) / Richa, Andrea (Committee member) / Arizona State University (Publisher)
Created2012
Description
A load balancer is an essential part of many network systems. A load balancer is capable of dividing and redistributing incoming network traffic to different back end servers, thus improving reliability and performance. Existing load balancing solutions can be classified into two categories: hardware-based or software-based. Hardware-based load balancing systems are hard to manage and force network administrators to scale up (replacing with more powerful but expensive hardware) when their system can not handle the growing traffic. Software-based solutions have a limitation when dealing with a single large TCP flow. In recent years, with the fast developments of virtualization technology, a new trend of network function virtualization (NFV) is being adopted. Instead of using proprietary hardware, an NFV network infrastructure uses virtual machines running to implement network functions such as load balancers, firewalls, etc. In this thesis, a new load balancing system is designed and evaluated. This system is high performance and flexible. It can fully utilize the bandwidth between a load balancer and back end servers compared to traditional load balancers such as HAProxy. The experimental results show that using this NFV load balancer could have $n$ ($n$ is the number of back end servers) times better performance than HAProxy. Also, an extract, transform and load (ETL) application was implemented to demonstrate that this load balancer can shorten data load time. The experiment shows that when loading a large data set (18.3GB), our load balancer needs only 28\% less time than traditional load balancer.
ContributorsWu, Jinxuan (Author) / Syrotiuk, Violet R. (Thesis advisor) / Bazzi, Rida (Committee member) / Huang, Dijiang (Committee member) / Arizona State University (Publisher)
Created2015
Description
Due to large data resources generated by online educational applications, Educational Data Mining (EDM) has improved learning effects in different ways: Students Visualization, Recommendations for students, Students Modeling, Grouping Students, etc. A lot of programming assignments have the features like automating submissions, examining the test cases to verify the correctness, but limited studies compared different statistical techniques with latest frameworks, and interpreted models in a unified approach.
In this thesis, several data mining algorithms have been applied to analyze students’ code assignment submission data from a real classroom study. The goal of this work is to explore
and predict students’ performances. Multiple machine learning models and the model accuracy were evaluated based on the Shapley Additive Explanation.
The Cross-Validation shows the Gradient Boosting Decision Tree has the best precision 85.93% with average 82.90%. Features like Component grade, Due Date, Submission Times have higher impact than others. Baseline model received lower precision due to lack of non-linear fitting.
In this thesis, several data mining algorithms have been applied to analyze students’ code assignment submission data from a real classroom study. The goal of this work is to explore
and predict students’ performances. Multiple machine learning models and the model accuracy were evaluated based on the Shapley Additive Explanation.
The Cross-Validation shows the Gradient Boosting Decision Tree has the best precision 85.93% with average 82.90%. Features like Component grade, Due Date, Submission Times have higher impact than others. Baseline model received lower precision due to lack of non-linear fitting.
ContributorsTian, Wenbo (Author) / Hsiao, Ihan (Thesis advisor) / Bazzi, Rida (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2019
Description
This study estimates the capitalization effect of golf courses in Maricopa County using the hedonic pricing method. It draws upon a dataset of 574,989 residential transactions from 2000 to 2006 to examine how the aesthetic, non-golf benefits of golf courses capitalize across a gradient of proximity measures. The measures for amenity value extend beyond home adjacency and include considerations for homes within a range of discrete walkability buffers of golf courses. The models also distinguish between public and private golf courses as a proxy for the level of golf course access perceived by non-golfers. Unobserved spatial characteristics of the neighborhoods around golf courses are controlled for by increasing the extent of spatial fixed effects from city, to census tract, and finally to 2000 meter golf course ‘neighborhoods.’ The estimation results support two primary conclusions. First, golf course proximity is found to be highly valued for adjacent homes and homes up to 50 meters way from a course, still evident but minimal between 50 and 150 meters, and insignificant at all other distance ranges. Second, private golf courses do not command a higher proximity premia compared to public courses with the exception of homes within 25 to 50 meters of a course, indicating that the non-golf benefits of courses capitalize similarly, regardless of course type. The results of this study motivate further investigation into golf course features that signal access or add value to homes in the range of capitalization, particularly for near-adjacent homes between 50 and 150 meters thought previously not to capitalize.
ContributorsJoiner, Emily (Author) / Abbott, Joshua (Thesis director) / Smith, Kerry (Committee member) / Economics Program in CLAS (Contributor) / School of Sustainability (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
This paper will focus on the changes in China's OFDI while also explaining its growth. However, another primary focus will be comparing the relationships between China, Hong Kong, and Africa. This paper will show the correlating changes between the three regions and explain the distribution of China's investments. One argument is that Hong Kong may play a large role in facilitating Chinese investment into Africa, which if not disaggregated, could lead to inaccurate numbers of China's FDI into Africa. The purpose of this paper is to investigate the importance of China's relationship with Hong Kong and Africa. In 2012, Garth Shelton argued that Hong Kong was an important gateway in South Africa's trade with China. Since then, many others have made similar claims in support of Hong Kong's bigger role. However, due to the difficulty of finding specific data for each region, these analyses are incomplete and fail to clearly substantiate their theory. I will try to find a correlation by gathering my own data, tables, and through different interviews.
ContributorsSon, James (Author) / Simonson, Mark (Thesis director) / Iheduru, Okechukwu (Committee member) / Economics Program in CLAS (Contributor) / Department of Finance (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Pandora is a play exploring our relationship with gendered technology through the lens of artificial intelligence. Can women be subjective under patriarchy? Do robots who look like women have subjectivity? Hoping to create a better version of ourselves, The Engineer must navigate the loss of her creation, and Pandora must navigate their new world. The original premiere run was March 27-28, 2018, original cast: Caitlin Andelora, Rikki Tremblay, and Michael Tristano Jr.
ContributorsToye, Abigail Elizabeth (Author) / Linde, Jennifer (Thesis director) / Abele, Kelsey (Committee member) / Department of Information Systems (Contributor) / Economics Program in CLAS (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Sagebrush Coffee is a small business in Chandler, Arizona that purchases green beans, roasts them in small batches for quality, and ships fresh, gourmet roasted coffee beans across the nation. Deciding which coffee beans to buy and roast is one of the most crucial business decisions Sagebrush and other gourmet coffee roasters face. Further complicating this decision is the fact that coffee is a crop, and like all crops, has a specific growing season and the exact same product cannot usually be ordered from year to year, even if it proves to be successful. The goal of this research is to use data analytics and visualization to help Sagebrush make better purchasing decisions by identifying consumer purchasing trends and providing a recommendation for their portfolio mix. In the end, I found that Latin American coffees are popular with both returning and first-time customers, but a specific country of origin does not appear to be associated with the top coffee producing countries. Additionally, December is a critical month for Sagebrush and Sagebrush should make sure to target the states with the most sales: California, Pennsylvania, and New York. Arizona has growth potential as it is not one of the top three locations, despite the presence of a physical store. Also included in the following report is a portfolio recommendation suggesting how many of each product based on region, processing type, and roast level to carry in inventory.
ContributorsBlue, Jessica Morgan (Author) / Kellso, James (Thesis director) / Davila, Eddie (Committee member) / Department of Information Systems (Contributor) / Economics Program in CLAS (Contributor) / Department of Supply Chain Management (Contributor) / Morrison School of Agribusiness (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
This thesis takes the form of a market research report with the goal of analyzing the implications of the United Kingdom (UK) leaving the European Union (EU) (known as “Brexit”) on London’s office commercial real estate market. The ultimate goal of this report is to make a prediction, firmly grounded in quantitative and qualitative research conducted over the past several months, as to the direction of London’s commercial real estate market going forward (post-Brexit). Within the commercial real estate sector, this paper narrows its focus to the office segment of the London market.
Understanding the political landscape is crucial to formulating a reasonable prediction as to the future of the London market. Aside from research reports and articles, our main insights into the political direction of Brexit come from our recordings from meetings in March of 2017 with two high-ranking members of Parliament and one member of the House of Lords—all of whom are members of the Tory Party (the meetings being held under the condition of anonymity). The below analysis will be followed by a discussion of the economics of Brexit, primarily focusing on the economic risks and uncertainties which have emerged after the vote, and which currently exist today. Such risks include the UK losing its financial passporting rights, weakening GDP and currency value, the potential for a reduction in foreign direct investment (FDI), and the potential loss of the service sector in the city of London due to not being able to access the European Single Market.
The report will shift focus to analyzing three competing viewpoints of the direction of the London market based on recordings from interviews of stakeholders in the London real estate market. One being an executive of one of the largest REITs in the UK, another being the Global Head of Real Estate at a top asset management firm, and another being a director at a large property consulting firm. The report includes these differing “sub-theses” in order to try to make sense of the vast market uncertainties post-Brexit as well as to contrast their viewpoints with where the market is currently and with the report’s investment recommendation.
The remainder of the report will consist of the methods used for analyzing market trends including how the data was modeled in order to make the investment recommendation. The report will analyze real estate and market metrics pre-Brexit, immediately after the vote, post-Brexit, and will conclude with future projections encapsulating the investment recommendation.
Understanding the political landscape is crucial to formulating a reasonable prediction as to the future of the London market. Aside from research reports and articles, our main insights into the political direction of Brexit come from our recordings from meetings in March of 2017 with two high-ranking members of Parliament and one member of the House of Lords—all of whom are members of the Tory Party (the meetings being held under the condition of anonymity). The below analysis will be followed by a discussion of the economics of Brexit, primarily focusing on the economic risks and uncertainties which have emerged after the vote, and which currently exist today. Such risks include the UK losing its financial passporting rights, weakening GDP and currency value, the potential for a reduction in foreign direct investment (FDI), and the potential loss of the service sector in the city of London due to not being able to access the European Single Market.
The report will shift focus to analyzing three competing viewpoints of the direction of the London market based on recordings from interviews of stakeholders in the London real estate market. One being an executive of one of the largest REITs in the UK, another being the Global Head of Real Estate at a top asset management firm, and another being a director at a large property consulting firm. The report includes these differing “sub-theses” in order to try to make sense of the vast market uncertainties post-Brexit as well as to contrast their viewpoints with where the market is currently and with the report’s investment recommendation.
The remainder of the report will consist of the methods used for analyzing market trends including how the data was modeled in order to make the investment recommendation. The report will analyze real estate and market metrics pre-Brexit, immediately after the vote, post-Brexit, and will conclude with future projections encapsulating the investment recommendation.
ContributorsHorn, Jonathan (Co-author) / Sidi, Adam (Co-author) / Bonadurer, Werner (Thesis director) / McDaniel, Cara (Committee member) / Department of Finance (Contributor) / School of Politics and Global Studies (Contributor) / Economics Program in CLAS (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12