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Black-Box Attack-Based Security Evaluation Framework for Credit Card Fraud Detection Models.

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Title: Black-Box Attack-Based Security Evaluation Framework for Credit Card Fraud Detection Models.
Authors: Xiao, Jin1, xiaojin@scu.edu.cn, Tian, Yuhang1, tyh70537@outlook.com, Jia, Yanlin2, yelinjyl@126.com, Jiang, Xiaoyi3, xjiang@uni-muenster.de, Yu, Lean1, yulean@amss.ac.cn, Wang, Shouyang4, sywang@amss.ac.cn
Source: INFORMS Journal on Computing; Sep/Oct2023, Vol. 35 Issue 5, p986-1001, 16p
Database: Applied Science & Technology Source
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Header DbId: aci
DbLabel: Applied Science & Technology Source
An: 172829196
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  Data: Black-Box Attack-Based Security Evaluation Framework for Credit Card Fraud Detection Models.
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  Data: <searchLink fieldCode="AU" term="%22Xiao%2C+Jin%22">Xiao, Jin</searchLink><relatesTo>1</relatesTo>, <i>xiaojin@scu.edu.cn</i><br /><searchLink fieldCode="AU" term="%22Tian%2C+Yuhang%22">Tian, Yuhang</searchLink><relatesTo>1</relatesTo>, <i>tyh70537@outlook.com</i><br /><searchLink fieldCode="AU" term="%22Jia%2C+Yanlin%22">Jia, Yanlin</searchLink><relatesTo>2</relatesTo>, <i>yelinjyl@126.com</i><br /><searchLink fieldCode="AU" term="%22Jiang%2C+Xiaoyi%22">Jiang, Xiaoyi</searchLink><relatesTo>3</relatesTo>, <i>xjiang@uni-muenster.de</i><br /><searchLink fieldCode="AU" term="%22Yu%2C+Lean%22">Yu, Lean</searchLink><relatesTo>1</relatesTo>, <i>yulean@amss.ac.cn</i><br /><searchLink fieldCode="AU" term="%22Wang%2C+Shouyang%22">Wang, Shouyang</searchLink><relatesTo>4</relatesTo>, <i>sywang@amss.ac.cn</i>
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PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=172829196
RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1287/ijoc.2023.1297
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      – Code: eng
        Text: English
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        PageCount: 16
        StartPage: 986
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      – TitleFull: Black-Box Attack-Based Security Evaluation Framework for Credit Card Fraud Detection Models.
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            NameFull: Tian, Yuhang
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            – D: 01
              M: 09
              Text: Sep/Oct2023
              Type: published
              Y: 2023
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              Value: 35
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