Parallel Outlier Detection in Dial-Back Fraud Calls

Bin Wu; Fan Di  Liao; Di Zhang

doi:10.4028/www.scientific.net/AMR.756-759.3659

Paper Titles

Algebraic Techniques in Linear Cryptanalysis
p.3634

An Evaluation Method of Combat Service Operation Performance for Surface-to-Air Missile Based on FNN
p.3640

A New Hybrid GRASP with the Pilot Method Forthe Delay-Constrained Multicast Routing Problem
p.3647

Imbalanced Data Detection Kernel Method in Closed Systems
p.3652

Parallel Outlier Detection in Dial-Back Fraud Calls
p.3659

Application of Two Unknown Quantities and Three Points Interpolation in the Atmospheric Transmittance of Infrared Radiation
p.3665

Retrieval Technology by Example during Product Development of Auto Parts
p.3669

Gear Fault Diagnosis Method of Intelligence Based on Genetic Algorithm to Optimize the BP Neural Network
p.3674

A Rescheduling Algorithm of Train-Group for Railway Emergencies and its Parallelization
p.3680

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 756-759Parallel Outlier Detection in Dial-Back Fraud...

Parallel Outlier Detection in Dial-Back Fraud Calls

Abstract:

With the intensified competition among telecommunications industry, we focused much on the quality of service. Illegal activities, especially dial-back fraud calls, may cause annoyance and inconvenience which will reduce user experience. The detection of dial-back fraud calls is an urgent issue that needs to be addressed. The rapid development of information technology which gives rise to the accumulated huge data will pose a greater challenge. However, traditional detecting methods to identify illegal activities cannot get acceptable accuracy. On the other hand, those methods become very inefficient or even unavailable when processing massive data. In this paper, we introduce a distributed outlier detection approach to locate illegal acts of the illegal users who have the characteristics as outliers. For a higher hit rate, we combine outlier detection with cluster coefficient. Besides, the method exploits parallel computation based on MapReduce in order to obtain vast time savings and improve the processing capability of the algorithm on large data. Extensive experimental results demonstrate the efficiently performances of proposed algorithm according to the evaluation criterions of speedup and scale up.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 756-759)

Pages:

3659-3664

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.756-759.3659

Citation:

Cite this paper

Online since:

September 2013

Authors:

Bin Wu, Fan Di Liao, Di Zhang

Keywords:

Cluster Coefficient, Illegal Activities, MapReduce, Outlier Detection, Parallelization

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Lammel.R., Google's MapReduce Programming Model-revisited, Science of Computer Programming, 2008, 70(1): 1-30.

Google Scholar

[2] Hadoop: Open source implementation of MapReduce.

Google Scholar

[3] Han Jiawei, Micheline. K , Data Mining: concepts and techniques, 2nd edition, San Froncisco, Morgan Kaufmann Publishers, (2006).

Google Scholar

[4] M.E.J. Newman, Scientific collaboration networks.I. Network construction and fundamental results, Phys. Rev. E, 2001, 64: 016131.

Google Scholar

[5] MM Breunig, HP Kriegel, RT Ng, J Sander, LOF: identifying density-based local outliers, In Proceedings of the ACM SIGMOD Conference, Dallas, TX, May (2000).

DOI: 10.1145/335191.335388

Google Scholar

[6] S Kim, NW Cho, B Kang, SH Kang, Fast outlier detection for very large log data, Expert Systems with Applications, 38 (2011) 9587–9596, (2011).

DOI: 10.1016/j.eswa.2011.01.162

Google Scholar