Near-Real Time SIM-box Fraud Detection Using Machine Learning in the case of ethio telecom
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Date
2020-02-28
Authors
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Journal ISSN
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Publisher
Addis Ababa University
Abstract
The advancement of telecommunication era is rapidly growing, however, telecom
fraudsters encouraged by the emerging of these new technologies. Interconnect
bypass fraud is one of the most sever threats to telecom operators. Subscriber
Identity Module Box (SIM-box) fraud is one of an interconnect bypass telecom
fraud type and uses Voice over IP (VoIP) technology. In addition, it’s difficult to
detect such fraud types with Test Call Generation (TCG) and a traditional types
of Fraud Management System (FMS). Both TCG and FMS easily bypassed by the
fraudsters, telecom companies impacted by losing billions of dollars.
In this study, Sliding Window (SW) aggregation mode is applied to provide a relevant
dataset instance and reduce detection delay to one hour by using supervised
Machine Learning (ML) algorithm. Three supervised ML classifier algorithms were
used, namely Random Forest (RF), Artificial Neural Network (ANN), and Support
Vector Machine (SVM) with the two validation techniques 10
-fold cross-validation
and supplied test. Call Detail Record (CDR) data were collected, relevant attributes
were selected and preprocessing such as data cleaning, integrating and aggregating
tasks were performed.
The experimental results depict that RF classifier using cross-validation on SW aggregation
mode achieves a better classification accuracy (96
.2
%). ANN is placed on
second with its overall performance accuracy and its detection delay, SVM algorithm
using cross-validation exceeds the desired detection delay (49
,965
second)
with poor performance accuracy. RF classifier algorithm using SW aggregation
mode overcomes the trade-off detection accuracy and detection delay.
Description
Keywords
Bypass Fraud, Machine Learning, SIM-box Fraud, Sliding window