Online Credit Card Fraud Detection: A Hybrid Framework with Big Data Technologies
Online Credit Card Fraud Detection: A Hybrid Framework with Big Data Technologies
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在线信用卡欺诈检测：大数据技术的混合框架
DOI：
10.1109/trustcom.2016.0253
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
M. Guo
中科院分区：
文献类型：
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作者：
You Dai;Jin Yan;Xiaoxin Tang;Han Zhao;M. Guo
在本文中，我们重点设计了一个基于大数据技术的在线信用卡欺诈检测框架，我们希望通过该框架实现三个主要目标：1)融合多种检测模型以提高准确性的能力；2)处理大数据的能力；3)实时检测的能力。为了实现这一目标，我们提出了一个通用的工作流程，它满足了当前大多数信用卡欺诈检测系统的设计思想。在此基础上，采用分布式存储层、批量训练层、键值共享层和流检测层四层架构实现工作流。通过这四层，我们可以分别支持海量交易数据存储、快速检测模型训练、快速模型数据共享和实时在线欺诈检测。我们采用Hadoop、Spark、Storm、HBase等最新的大数据技术实现。在一个合成数据集上实现了一个原型并进行了测试，显示出实现上述目标的巨大潜力。
In this paper, we focus on designing an online credit card fraud detection framework with big data technologies, by which we want to achieve three major goals: 1) the ability to fuse multiple detection models to improve accuracy, 2) the ability to process large amount of data and 3) the ability to do the detection in real time. To accomplish that, we propose a general workflow, which satisfies most design ideas of current credit card fraud detection systems. We further implement the workflow with a new framework which consists of four layers: distributed storage layer, batch training layer, key-value sharing layer and streaming detection layer. With the four layers, we are able to support massive trading data storage, fast detection model training, quick model data sharing and real-time online fraud detection, respectively. We implement it with latest big data technologies like Hadoop, Spark, Storm, HBase, etc. A prototype is implemented and tested with a synthetic dataset, which shows great potentials of achieving the above goals.