PFrauDetector: A parallelized graph mining approach for efficient fraudulent phone call detection

Josh Jia Ching Ying, Ji Zhang, Che Wei Huang, Kuan Ta Chen, Vincent S. Tseng*

*Corresponding author for this work

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

2 Scopus citations

Abstract

In recent years, fraud is becoming more rampant internationally with the development of modern technology and global communication. Due to the rapid growth in the volume of call logs, the task of fraudulent phone call detection is confronted with Big Data issues in real-world implementations. While our previous work, FrauDetector, has addressed this problem and achieved some promising results, it can be further enhanced as it focuses on the fraud detection accuracy while the efficiency and scalability are not on the top priority. Meanwhile, other known approaches suffer from long training time and/or cannot accurately detect fraudulent phone calls in real time. In this paper, we propose a highly-efficient parallelized graph-mining-based fraudulent phone call detection framework, namely PFrauDetector, which is able to automatically label fraudulent phone numbers with a 'fraud' tag, a crucial prerequisite for distinguishing fraudulent phone call numbers from the normal ones. PFrauDetector generates smaller, more manageable sub-networks from the original graph and performs a parallelized weighted HITS algorithm for significant speed acceleration in the graph learning module. It adopts a novel aggregation approach to generate the trust (or experience) value for each phone number (or user) based on their respective local values. We conduct a comprehensive experimental study based on a real dataset collected through an anti-fraud mobile application, Whoscall. The results demonstrate a significantly improved efficiency of our approach compared to FrauDetector and superior performance against other major classifier-based methods.

Original languageEnglish
Title of host publicationProceedings - 22nd IEEE International Conference on Parallel and Distributed Systems, ICPADS 2016
EditorsXiaofei Liao, Robert Lovas, Xipeng Shen, Ran Zheng
PublisherIEEE Computer Society
Pages1059-1066
Number of pages8
ISBN (Electronic)9781509044573
DOIs
StatePublished - 2 Jul 2016
Event22nd IEEE International Conference on Parallel and Distributed Systems, ICPADS 2016 - Wuhan, Hubei, China
Duration: 13 Dec 201616 Dec 2016

Publication series

NameProceedings of the International Conference on Parallel and Distributed Systems - ICPADS
Volume0
ISSN (Print)1521-9097

Conference

Conference22nd IEEE International Conference on Parallel and Distributed Systems, ICPADS 2016
CountryChina
CityWuhan, Hubei
Period13/12/1616/12/16

Keywords

  • Fraudulent Phone Call Detection
  • Parallelized Weighted HITS Algorithm
  • Telecommunication Fraud
  • Trust Value Mining

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  • Cite this

    Ying, J. J. C., Zhang, J., Huang, C. W., Chen, K. T., & Tseng, V. S. (2016). PFrauDetector: A parallelized graph mining approach for efficient fraudulent phone call detection. In X. Liao, R. Lovas, X. Shen, & R. Zheng (Eds.), Proceedings - 22nd IEEE International Conference on Parallel and Distributed Systems, ICPADS 2016 (pp. 1059-1066). [7823855] (Proceedings of the International Conference on Parallel and Distributed Systems - ICPADS; Vol. 0). IEEE Computer Society. https://doi.org/10.1109/ICPADS.2016.0140