Invariant Diversity as a Proactive Fraud Detection Mechanism for Online Merchants

被引：0

作者：

Laurens, Roy ^{[1
]}

Jusak, Jusak ^{[2
]}

Zou, Cliff C. ^{[1
]}

机构：

[1] Univ Cent Florida, Dept Comp Sci, Orlando, FL 32816 USA

[2] Inst Bisnis & Informat Stikom Surabaya, Dept Comp Engn, Surabaya, Indonesia

来源：

GLOBECOM 2017 - 2017 IEEE GLOBAL COMMUNICATIONS CONFERENCE | 2017年

基金：

美国国家科学基金会;

关键词：

Electronic Commerce; credit card fraud; fraud prevention; diversity index;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页数：6

共 16 条

[1]

Bolton R. J., 2001, P CRED SCOR CRED CON

[2]

Brabazon A., 2010, IEEE C EV COMP

[3]

Brause R., 1999, P 11 IEEE INT C TOOL

[4]

Conroy J., 2014, CARD NOT PRESENT FRA

[5]

Dai Y, 2016, IEEE TRUST BIG, P1644, DOI [10.1109/TrustCom.2016.0253, 10.1109/TrustCom.2016.251]

[6]

De Myttenaere A., COMPUTATIONAL INTELL

[7]

Duman E., 2011, EXPERT SYSTEM APPL, V38

[8]

Ghosh Sushmito, 1994, P 27 ANN HAW INT C S

[9]

Lee E., 2016, NETW DISTR SYST SSEC

[10]

Magurran A.E., 2003, Measuring Biological Diversity, P1

← 1 2 →