| With the rapid development of e-commerce,online trading has become very common in our daily life,which makes online payment service a major challenge.Among the many security issues,how to detect fraud transactions among the massive payment records is a key function to the many service providers,like banks and internet companies.Any delay or false detection of the fraud transactions could cause huge loss of profit.However,most previous works focus on improving detection accuracy while putting less effort on their data processing ability,which could not catch up with the burst trading data.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. |
