Predictive analytics to prevent voice over ip international revenue sharing fraud

Yoram J. Meijaard, Bram C.M. Cappers, Josh G.M. Mengerink, Nicola Zannone

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

Abstract

International Revenue Sharing Fraud (IRSF) is the most persistent type of fraud in the telco industry. Hackers try to gain access to an operator’s network in order to make expensive unauthorized phone calls on behalf of someone else. This results in massive phone bills that victims have to pay while number owners earn the money. Current anti-fraud solutions enable the detection of IRSF afterwards by detecting deviations in the overall caller’s expenses and block phone devices to prevent attack escalation. These solutions suffer from two main drawbacks: (i) they act only when financial damage is done and (ii) they offer no protection against future attacks. In this paper, we demonstrate how unsupervised machine learning can be used to discover fraudulent calls at the moment of their establishment, thereby preventing IRSF from happening. Specifically, we investigate the use of Isolation Forests for the detection of frauds before calls are initiated and compare the results to an existing industrial post-mortem anti-fraud solution.

Original languageEnglish
Title of host publicationData and Applications Security and Privacy - 34th Annual IFIP WG 11.3 Conference, DBSec 2020, Proceedings
EditorsAnoop Singhal, Jaideep Vaidya
PublisherSpringer
Pages241-260
Number of pages20
ISBN (Print)9783030496685
DOIs
Publication statusPublished - 2020
Event34th Annual IFIP WG11.3 Conference on Data and Applications Security and Privacy, DBSec 2020 - Regensburg, Germany
Duration: 25 Jun 202026 Jun 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12122 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference34th Annual IFIP WG11.3 Conference on Data and Applications Security and Privacy, DBSec 2020
CountryGermany
CityRegensburg
Period25/06/2026/06/20

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  • Cite this

    Meijaard, Y. J., Cappers, B. C. M., Mengerink, J. G. M., & Zannone, N. (2020). Predictive analytics to prevent voice over ip international revenue sharing fraud. In A. Singhal, & J. Vaidya (Eds.), Data and Applications Security and Privacy - 34th Annual IFIP WG 11.3 Conference, DBSec 2020, Proceedings (pp. 241-260). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12122 LNCS). Springer. https://doi.org/10.1007/978-3-030-49669-2_14