Abnormal Payment Transaction Detection Scheme Based on Scalable Architecture and Redis Cluster

Taeyoung Leea, Yongsung Kim, Een Jun Hwang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Log file based data analysis methods in the closed fault tolerant OS have shown several problems. First, it is not easy to add or change the data analysis direction while the service is running after the analysis process has been set and compiled. Second, in an independent closed system, due to the limited resource policy, it is difficult to perform real-time data analysis. Finally, it is not easy to utilize new technologies and open sources such as in-memory database and python. Due to these problems, existing methods have difficulty in detecting abnormal payment transactions in real time. In this paper, we propose an abnormal payment transaction detection scheme based on scalable network architecture and Redis cluster, which can collect transaction data quickly and perform their analysis in real-time. We show its performance by implementing a prototype system and performing several experiments on it. Furthermore, we show that our proposed scheme can be used for data analysis through the reproduction of data using in-memory storage, which can solve the aforementioned problem of unidirectional analysis by doing parallel processing on the distributed Redis repository.

Original languageEnglish
Title of host publication2018 International Conference on Platform Technology and Service, PlatCon 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538647103
DOIs
Publication statusPublished - 2018 Sep 25
Event2018 International Conference on Platform Technology and Service, PlatCon 2018 - Jeju, Korea, Republic of
Duration: 2018 Jan 292018 Jan 31

Other

Other2018 International Conference on Platform Technology and Service, PlatCon 2018
CountryKorea, Republic of
CityJeju
Period18/1/2918/1/31

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Data storage equipment
Network architecture
Processing
Experiments

Keywords

  • abnormality detection
  • in-memory computing
  • realtime analysis
  • scalable network architecture

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Hardware and Architecture
  • Computer Networks and Communications
  • Computer Science Applications

Cite this

Leea, T., Kim, Y., & Hwang, E. J. (2018). Abnormal Payment Transaction Detection Scheme Based on Scalable Architecture and Redis Cluster. In 2018 International Conference on Platform Technology and Service, PlatCon 2018 [8472732] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/PlatCon.2018.8472732

Abnormal Payment Transaction Detection Scheme Based on Scalable Architecture and Redis Cluster. / Leea, Taeyoung; Kim, Yongsung; Hwang, Een Jun.

2018 International Conference on Platform Technology and Service, PlatCon 2018. Institute of Electrical and Electronics Engineers Inc., 2018. 8472732.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Leea, T, Kim, Y & Hwang, EJ 2018, Abnormal Payment Transaction Detection Scheme Based on Scalable Architecture and Redis Cluster. in 2018 International Conference on Platform Technology and Service, PlatCon 2018., 8472732, Institute of Electrical and Electronics Engineers Inc., 2018 International Conference on Platform Technology and Service, PlatCon 2018, Jeju, Korea, Republic of, 18/1/29. https://doi.org/10.1109/PlatCon.2018.8472732
Leea T, Kim Y, Hwang EJ. Abnormal Payment Transaction Detection Scheme Based on Scalable Architecture and Redis Cluster. In 2018 International Conference on Platform Technology and Service, PlatCon 2018. Institute of Electrical and Electronics Engineers Inc. 2018. 8472732 https://doi.org/10.1109/PlatCon.2018.8472732
Leea, Taeyoung ; Kim, Yongsung ; Hwang, Een Jun. / Abnormal Payment Transaction Detection Scheme Based on Scalable Architecture and Redis Cluster. 2018 International Conference on Platform Technology and Service, PlatCon 2018. Institute of Electrical and Electronics Engineers Inc., 2018.
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