Using association rules for fraud detection in web advertising networks

Ahmed Metwally, Divyakant Agrawal, Amr El Abbadi

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

46 Citations (Scopus)

Abstract

Discovering associations between elements occurring in a stream is applicable in numerous applications, including predictive caching and fraud detection. These applications require a new model of association between pairs of elements in streams. We develop an algorithm, Stream ing-Rules, to report association rules with tight guarantees on errors, using limited processing per element, and minimal space. The modular design of Stream ing-Rules allows for integration with current stream management systems, since it employs existing techniques for finding frequent elements. The presentation emphasizes the applicability of the algorithm to fraud detection in advertising networks. Such fraud instances have not been successfully detected by current techniques. Our experiments on synthetic data demonstrate scalability and efficiency. On real data, potential fraud was discovered.

Original languageEnglish
Title of host publicationVLDB 2005 - Proceedings of 31st International Conference on Very Large Data Bases
Pages169-180
Number of pages12
Publication statusPublished - 1 Dec 2005
EventVLDB 2005 - 31st International Conference on Very Large Data Bases - Trondheim, Norway
Duration: 30 Aug 20052 Sep 2005

Publication series

NameVLDB 2005 - Proceedings of 31st International Conference on Very Large Data Bases
Volume1

Other

OtherVLDB 2005 - 31st International Conference on Very Large Data Bases
CountryNorway
CityTrondheim
Period30/8/052/9/05

    Fingerprint

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Metwally, A., Agrawal, D., & El Abbadi, A. (2005). Using association rules for fraud detection in web advertising networks. In VLDB 2005 - Proceedings of 31st International Conference on Very Large Data Bases (pp. 169-180). (VLDB 2005 - Proceedings of 31st International Conference on Very Large Data Bases; Vol. 1).