HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Conference papers

Predictive Analytics to Prevent Voice over IP International Revenue Sharing Fraud

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.
Document type :
Conference papers
Complete list of metadata

https://hal.inria.fr/hal-03243633
Contributor : Hal Ifip Connect in order to contact the contributor
Submitted on : Monday, May 31, 2021 - 5:52:57 PM
Last modification on : Monday, May 31, 2021 - 6:08:54 PM

File

 Restricted access
To satisfy the distribution rights of the publisher, the document is embargoed until : 2023-01-01

Please log in to resquest access to the document

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Citation

Yoram Meijaard, Bram Cappers, Josh Mengerink, Nicola Zannone. Predictive Analytics to Prevent Voice over IP International Revenue Sharing Fraud. 34th IFIP Annual Conference on Data and Applications Security and Privacy (DBSec), Jun 2020, Regensburg, Germany. pp.241-260, ⟨10.1007/978-3-030-49669-2_14⟩. ⟨hal-03243633⟩

Share

Metrics

Record views

29