Abstract
The majority of online credit/debit card fraud research focuses on the defense by back-end entities, such as card issuer or processor (i.e., payment processing company), and overlooks the fraud defense initiated by online merchants. This is problematic because the merchants – especially online merchants – are the ones generally held responsible for covering any loss due to transaction fraud. Thus they have a great incentive to detect and defend against card fraud. But at the same time, compared with card issuers, they also lack access to large samples needed for data mining (such as existing purchase data of a cardholder). This dissertation presents a novel semi-autonomous framework for online merchants to defend against such fraud by utilizing three interrelated components: a supervised classifier based on existing fraud pattern and our newly developed DNS fingerprinting, an unsupervised anomaly detection method using diversity index, and a novel soft descriptor based verification system. The classifier and the anomaly detection work together to allow our framework to detect known fraud patterns and adapt to the previously undetected patterns. Afterward, suspicious transactions can be autonomously verified by requesting the customer to provide a unique identifier that was previously embedded in the soft descriptor during the card transaction processing. This verification process greatly improves fraud detection accuracy without adding a burden on most legitimate customers. Our framework can be readily implemented and we have deployed several aspects of our framework at a real-world e-commerce Merchant website, with the real testing results explained in this dissertation.
Notes
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Graduation Date
2023
Semester
Spring
Advisor
Zou, Cliff
Degree
Doctor of Philosophy (Ph.D.)
College
College of Engineering and Computer Science
Department
Computer Science
Degree Program
Computer Science
Identifier
CFE0009558; DP0027567
URL
https://purls.library.ucf.edu/go/DP0027567
Language
English
Release Date
May 2026
Length of Campus-only Access
3 years
Access Status
Doctoral Dissertation (Campus-only Access)
STARS Citation
Laurens, Roy, "A Semi-Autonomous Credit/Debit Card Transaction Fraud Defense Framework for Online Merchants" (2023). Electronic Theses and Dissertations, 2020-2023. 1601.
https://stars.library.ucf.edu/etd2020/1601
Restricted to the UCF community until May 2026; it will then be open access.