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.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2020-2600 |
| Date | 01 January 2023 |
| Creators | Laurens, Roy |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations, 2020- |
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