Thesis Open Access
tewodros hailu
<?xml version='1.0' encoding='UTF-8'?> <record xmlns="http://www.loc.gov/MARC21/slim"> <leader>00000nam##2200000uu#4500</leader> <controlfield tag="005">20241202095156.0</controlfield> <controlfield tag="001">4488</controlfield> <datafield tag="856" ind1="4" ind2=" "> <subfield code="s">647925</subfield> <subfield code="z">md5:d45cbd5fd89e1d88df48cadeabb670f7</subfield> <subfield code="u">https://zenodo.org/record/4488/files/f1042664640.pdf</subfield> </datafield> <datafield tag="542" ind1=" " ind2=" "> <subfield code="l">open</subfield> </datafield> <datafield tag="260" ind1=" " ind2=" "> <subfield code="c">2018-11-14</subfield> </datafield> <datafield tag="909" ind1="C" ind2="O"> <subfield code="p">user-aau</subfield> <subfield code="p">user-zenodo</subfield> <subfield code="o">oai:zenodo.org:4488</subfield> </datafield> <datafield tag="100" ind1=" " ind2=" "> <subfield code="a">tewodros hailu</subfield> </datafield> <datafield tag="245" ind1=" " ind2=" "> <subfield code="a">Network Traffic Classification Using Machine Learning: A Step Towards Over-the-Top Bypass Fraud Detection</subfield> </datafield> <datafield tag="980" ind1=" " ind2=" "> <subfield code="a">user-aau</subfield> </datafield> <datafield tag="980" ind1=" " ind2=" "> <subfield code="a">user-zenodo</subfield> </datafield> <datafield tag="540" ind1=" " ind2=" "> <subfield code="u">http://www.opendefinition.org/licenses/cc-by</subfield> <subfield code="a">Creative Commons Attribution</subfield> </datafield> <datafield tag="650" ind1="1" ind2="7"> <subfield code="a">cc-by</subfield> <subfield code="2">opendefinition.org</subfield> </datafield> <datafield tag="520" ind1=" " ind2=" "> <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> </datafield> <datafield tag="773" ind1=" " ind2=" "> <subfield code="n">doi</subfield> <subfield code="i">isVersionOf</subfield> <subfield code="a">10.20372/nadre:4487</subfield> </datafield> <datafield tag="024" ind1=" " ind2=" "> <subfield code="a">10.20372/nadre:4488</subfield> <subfield code="2">doi</subfield> </datafield> <datafield tag="980" ind1=" " ind2=" "> <subfield code="a">publication</subfield> <subfield code="b">thesis</subfield> </datafield> </record>
All versions | This version | |
---|---|---|
Views | 0 | 0 |
Downloads | 0 | 0 |
Data volume | 0 Bytes | 0 Bytes |
Unique views | 0 | 0 |
Unique downloads | 0 | 0 |