An application of decision trees for rule extraction towards telecommunications fraud detection

Constantinos S. Hilas, John N. Sahalos

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Telecommunications fraud has drawn the attention in research due to the huge economic burden on companies and to the interesting aspect of users' behavior modeling. In the present paper, an application of decision trees to fraud detection is presented. The appropriate user behavior modeling is, also, discussed. The trees are used for rule extraction in order to distinguish between normal and fraudulent activities in a telecommunications network. Several real cases of defrauded user accounts are modeled by means of selected usage features. Decision trees are applied in order to identify the critical values that separate fraud from legal use. These thresholds are expressed in the form of rules that will be used in a rule based expert system.

Original languageEnglish
Title of host publicationKnowledge-Based Intelligent Information and Engineering Systems
Subtitle of host publicationKES 2007 - WIRN 2007 - 11th International Conference, KES 2007, XVII Italian Workshop on Neural Networks, Proceedings
Pages1112-1121
Number of pages10
Volume4693 LNAI
EditionPART 2
DOIs
Publication statusPublished - 2007
Event11th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2007, and 17th Italian Workshop on Neural Networks, WIRN 2007 - Vietri sul Mare, Italy
Duration: 12 Sept 200714 Sept 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume4693 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other11th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2007, and 17th Italian Workshop on Neural Networks, WIRN 2007
Country/TerritoryItaly
CityVietri sul Mare
Period12/09/0714/09/07

Keywords

  • Feature selection
  • Fraud detection
  • Rule extraction
  • Telecommunications
  • User profiles

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