Thesis Open Access
tewodros hailu
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<subfield code="a">Network Traffic Classification Using Machine Learning: A Step Towards Over-the-Top Bypass Fraud Detection</subfield>
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<subfield code="a"><p>Over-the-Top (OTT) bypass is a type of Interconnect Bypass fraud where regular<br>
voice calls are rerouted through OTT network and terminated as an OTT call. These<br>
calls are terminated using OTT applications which need user&rsquo;s Mobile Station International<br>
Subscriber Directory Number (MSISDN) for authentication. Detecting<br>
OTT voice call packets through different network traffic classification techniques is<br>
one subtask in the detection of this fraud.<br>
In this thesis, performance of three machine learning algorithms; Adaptive Booster<br>
(AdaBoost) + J48, Repeated Incremental Pruning to Produce Error Reduction (RIPPER),<br>
and Support Vector Machine (SVM) is evaluated in detecting MSISDN-based OTT<br>
packets taking Viber, Tango, and Telegram as a sample. Detection of OTT traffic<br>
and voice call packets from the OTT traffic have been treated separately as classification<br>
tasks. Ten cross-fold and separate test data validation techniques together<br>
with 1.7 million labeled packets generated and captured in controlled laboratory<br>
environment are used in the evaluation process.<br>
AdaBoost + J48 achieved the best accuracy on both classification tasks compared to<br>
the others while using ten cross-fold validation. However, an accuracy of 48.4%<br>
obtained in detecting voice call packets while using separate test data validation<br>
makes it less preferable in the classification task. Even if it takes longer time to<br>
train SVM, it was the best performer (95.35% accurate) in detecting voice call packets<br>
in separate test data validation. Considering accuracy attained by the algorithms<br>
in separate test data validation technique together with the detection rate<br>
of OTT voice call packets, SVM is preferable than the other two algorithms</p></subfield>
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