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

Ying, Josh Jia-Ching and Zhang, Ji and Huang, Che-Wei and Chen, Kuan-Ta and Tseng, Vincent S. (2016) PFrauDetector: a parallelized graph mining approach for efficient fraudulent phone call detection. In: 22nd IEEE International Conference on Parallel and Distributed Systems (ICPADS 2016), 13-16 Dec 2016, Wuhan, China.

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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-miningbased 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 subnetworks
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.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: Accepted version deposited in accordance with the copyright policy of the publisher.
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 20 Feb 2017 06:12
Last Modified: 05 Jun 2017 01:13
Uncontrolled Keywords: telecommunication fraud; trust value mining; fraudulent phone call detection; parallelized weighted HITS algorithm
Fields of Research : 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
Identification Number or DOI: 10.1109/ICPADS.2016.0140
URI: http://eprints.usq.edu.au/id/eprint/30393

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