Laurens, Roy, Jusak, Jusak, and Zou, Cliff (2018) Invariant diversity as a proactive fraud detection mechanism for online merchants. In: Proceedings of the IEEE Global Communications Conference. From: GLOBECOM 2017: IEEE Global Communications Conference, 4-8 December 2017, Singapore.

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Abstract

Online merchants face difficulties in using existing card fraud detection algorithms, so in this paper we propose a novel proactive fraud detection model using what we call invariant diversity to reveal patterns among attributes of the devices (computers or smartphones) that are used in conducting the transactions. The model generates a regression function from a diversity index of various attribute combinations, and use it to detect anomalies inherent in certain fraudulent transactions. This approach allows for proactive fraud detection using a relatively small number of unsupervised transactions and is resistant to fraudsters’ device obfuscation attempt. We tested our system successfully on real online merchant transactions and it managed to find several instances of previously undetected fraudulent transactions.

Item ID:	70990
Item Type:	Conference Item (Research - E1)
ISBN:	978-1-5090-5019-2
Keywords:	Electronic Commerce; credit card fraud; fraud prevention; diversity index
Related URLs:	Publisher
Copyright Information:	© IEEE
Funders:	National Science Foundation (NSF)
Projects and Grants:	NSF grant SaTC-EDU-1723587
Date Deposited:	07 Dec 2021 01:15
FoR Codes:	46 INFORMATION AND COMPUTING SCIENCES > 4604 Cybersecurity and privacy > 460403 Data security and protection @ 50% 46 INFORMATION AND COMPUTING SCIENCES > 4609 Information systems > 460904 Information security management @ 50%
SEO Codes:	22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220405 Cybersecurity @ 70% 22 INFORMATION AND COMMUNICATION SERVICES > 2203 Information services > 220302 Electronic information storage and retrieval services @ 30%
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