TY - GEN
T1 - Predictive analytics to prevent voice over ip international revenue sharing fraud
AU - Meijaard, Yoram J.
AU - Cappers, Bram C.M.
AU - Mengerink, Josh G.M.
AU - Zannone, Nicola
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85087531209&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-49669-2_14
DO - 10.1007/978-3-030-49669-2_14
M3 - Conference contribution
AN - SCOPUS:85087531209
SN - 9783030496685
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 241
EP - 260
BT - Data and Applications Security and Privacy - 34th Annual IFIP WG 11.3 Conference, DBSec 2020, Proceedings
A2 - Singhal, Anoop
A2 - Vaidya, Jaideep
PB - Springer
T2 - 34th Annual IFIP WG11.3 Conference on Data and Applications Security and Privacy, DBSec 2020
Y2 - 25 June 2020 through 26 June 2020
ER -