Matching Items (4)
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- All Subjects: Academic Integrity
- All Subjects: Phishing
- Creators: Bazzi, Rida
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
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
Description
Academic integrity among college students continues to be a problem at colleges and universities. This is particularly important for journalism students where ethical issues in the profession are critical, especially in an era of “fake news” and distrust in the media. While most journalism students study professional ethics, they do not necessarily make the connection between their future careers and their academic career. In fact, at Western Washington University (Western) a recent exploration into academic dishonesty revealed that violations were increasing, and that journalism was one of the top three majors where violations occurred (based on percent of majors). To address this problem of practice, an online academic integrity resource – specific to journalism – was developed to see whether it could increase students’ knowledge as it relates to academic integrity and decrease violations. The mixed methods action research (MMAR) study took place during summer and fall quarter at Western Washington University, a state university located in Bellingham, Washington. Participants included students who were pre-majors, majors, and minors in the three tracks of journalism: news-editorial, public relations, and visual journalism. They were given multiple opportunities to self-enroll in the Resource for Ethical Academic Development (READ) Canvas course for academic integrity. Self-efficacy theory and social learning theory provided a framework for the study. Data was collected through pre- and post-innovation surveys as well as qualitative interviews. Quantitative results suggest that there is work yet to do in order to educate students about academic integrity and potential consequences of behavior. Qualitative results suggest that one avenue may be through an online resource that provides concise and comprehensive information, models behavior relevant to the student’s own discipline, and is easily accessible. It also suggests that a culture change from a systemic emphasis on grades to a focus on growth and individual learning may be beneficial.
ContributorsKeller, Jennifer Margaret (Author) / Henriksen, Danah (Thesis advisor) / Silcock, Bill (Committee member) / VanderStaay, Steven (Committee member) / Arizona State University (Publisher)
Created2021
DescriptionThe goal of this study is to equip administrators and instructors with a deeper understanding of the apparent cheating problem in Computer Science courses, with proposed solutions to lower academic dishonesty from the students’ perspective.
ContributorsAl Yasari, Farah (Co-author) / Alyasari, Farah (Co-author) / Tadayon-Navabi, Farideh (Thesis director) / Bazzi, Rida (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05