Invariant Diversity as a Proactive Fraud Detection Mechanism for Online Merchants
| Main Authors: | Laurens, Roy, Jusak, Jusak, Zou, Cliff C. |
|---|---|
| Format: | Proceeding PeerReviewed Book |
| Bahasa: | eng |
| Terbitan: |
, 2017
|
| Subjects: | |
| Online Access: |
https://repository.dinamika.ac.id/id/eprint/5603/1/1.%20Dokumen%20Globecom2017.pdf https://repository.dinamika.ac.id/id/eprint/5603/2/2.%20Peer%20Review%20Blobecom17.pdf https://repository.dinamika.ac.id/id/eprint/5603/3/3.%20Turnitin%20GLOBECOM2017.pdf https://repository.dinamika.ac.id/id/eprint/5603/ |
| ctrlnum |
5603 |
|---|---|
| fullrecord |
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<dc schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><relation>https://repository.dinamika.ac.id/id/eprint/5603/</relation><title>Invariant Diversity as a Proactive Fraud Detection Mechanism for Online Merchants</title><creator>Laurens, Roy</creator><creator>Jusak, Jusak</creator><creator>Zou, Cliff C.</creator><subject>005 Computer programming, programs & data</subject><description>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.</description><date>2017</date><type>Journal:Proceeding</type><type>PeerReview:PeerReviewed</type><type>Book:Book</type><language>eng</language><identifier>https://repository.dinamika.ac.id/id/eprint/5603/1/1.%20Dokumen%20Globecom2017.pdf</identifier><type>Book:Book</type><language>eng</language><identifier>https://repository.dinamika.ac.id/id/eprint/5603/2/2.%20Peer%20Review%20Blobecom17.pdf</identifier><type>Book:Book</type><language>eng</language><identifier>https://repository.dinamika.ac.id/id/eprint/5603/3/3.%20Turnitin%20GLOBECOM2017.pdf</identifier><identifier> Laurens, Roy, Jusak, Jusak ORCID: https://orcid.org/0000-0001-5646-4865 <https://orcid.org/0000-0001-5646-4865> and Zou, Cliff C. (2017) Invariant Diversity as a Proactive Fraud Detection Mechanism for Online Merchants. In: IEEE Global Communications Conference (GLOBECOM), 4-8 December 2017, Singapore. </identifier><recordID>5603</recordID></dc>
|
| language |
eng |
| format |
Journal:Proceeding Journal PeerReview:PeerReviewed PeerReview Book:Book Book |
| author |
Laurens, Roy Jusak, Jusak Zou, Cliff C. |
| title |
Invariant Diversity as a Proactive Fraud Detection Mechanism for Online Merchants |
| publishDate |
2017 |
| isbn |
0000000156464 |
| topic |
005 Computer programming programs & data |
| url |
https://repository.dinamika.ac.id/id/eprint/5603/1/1.%20Dokumen%20Globecom2017.pdf https://repository.dinamika.ac.id/id/eprint/5603/2/2.%20Peer%20Review%20Blobecom17.pdf https://repository.dinamika.ac.id/id/eprint/5603/3/3.%20Turnitin%20GLOBECOM2017.pdf https://repository.dinamika.ac.id/id/eprint/5603/ |
| contents |
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. |
| id |
IOS16212.5603 |
| institution |
Universitas Dinamika |
| institution_id |
3669 |
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library:university library |
| library |
Perpustakaan Universitas Dinamika |
| library_id |
4565 |
| collection |
Repository Universitas Dinamika |
| repository_id |
16212 |
| city |
KOTA SURABAYA |
| province |
JAWA TIMUR |
| repoId |
IOS16212 |
| first_indexed |
2021-10-29T02:45:02Z |
| last_indexed |
2021-10-29T02:45:02Z |
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dc |
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1714921005210664960 |
| score |
17.538404 |