Performance Evaluation of Unsupervised Learning Techniques for Enterprise Toll Fraud Detection
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Date
2018-11-16
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Abstract
In recent years, the impact of telecom frauds is increasing and their behavior
is changing through time which makes them to remain the main challenge for
telecom service providers in terms of revenue loss and degradation of quality of
service. Toll fraud occurs whenever a criminal uses cheating or dishonest means
with the intention to use telephony services free of charge, reduced rate or to
make money.
This study focuses on detection of toll fraud committed through enterprise PBX
hacking by analyzing Call Detail Record (CDR) data. Unfortunately, this data is
mostly unlabeled, meaning no indications on which calls are fraudulent or nonfraudulent
exist. A clustering model was developed and tested with CDR collected
from ethio telecom. In order to test the model with big dataset additional synthetic
CDR was also used in the research. Two user proļ¬le are constructed by summarizing
the data on daily basis. WEKA machine learning tool has been used to come
up with a model for predicting fraudulent activities.
The experimentation result showed that, the model from the K-means algorithm
exhibited higher accuracy level, and can be applied in toll fraud detection. Since it
was able to detect unusual changes in calling patterns which are highly likely as a
consequence of fraud. The implementation of the model will enable telecom operators
in general and ethio telecom in particular to detect such fraud at minimum
cost of operation. Moreover, it can also be used to support enterprise customers
by showing security vulnerability of their Private Branch eXchange (PBX).
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Keywords
Toll fraud, Unsupervised Learning, K-means, Expectation Maximization (EM)