Anomaly detection in a mobile data network
| dc.contributor.advisor | Ngwenya, Mzabalazo | |
| dc.contributor.author | Salzwedel, Jason Paul | |
| dc.date.accessioned | 2020-02-20T11:10:31Z | |
| dc.date.available | 2020-02-20T11:10:31Z | |
| dc.date.issued | 2019 | |
| dc.date.updated | 2020-02-14T12:21:35Z | |
| dc.description.abstract | The dissertation investigated the creation of an anomaly detection approach to identify anomalies in the SGW elements of a LTE network. Unsupervised techniques were compared and used to identify and remove anomalies in the training data set. This “cleaned” data set was then used to train an autoencoder in an semi-supervised approach. The resultant autoencoder was able to indentify normal observations. A subsequent data set was then analysed by the autoencoder. The resultant reconstruction errors were then compared to the ground truth events to investigate the effectiveness of the autoencoder’s anomaly detection capability. | |
| dc.identifier.apacitation | Salzwedel, J. P. (2019). <i>Anomaly detection in a mobile data network</i>. (). ,Faculty of Science ,Department of Statistical Sciences. Retrieved from http://hdl.handle.net/11427/31202 | en_ZA |
| dc.identifier.chicagocitation | Salzwedel, Jason Paul. <i>"Anomaly detection in a mobile data network."</i> ., ,Faculty of Science ,Department of Statistical Sciences, 2019. http://hdl.handle.net/11427/31202 | en_ZA |
| dc.identifier.citation | Salzwedel, J. 2019. Anomaly detection in a mobile data network. | en_ZA |
| dc.identifier.ris | TY - Thesis / Dissertation AU - Salzwedel, Jason Paul AB - The dissertation investigated the creation of an anomaly detection approach to identify anomalies in the SGW elements of a LTE network. Unsupervised techniques were compared and used to identify and remove anomalies in the training data set. This “cleaned” data set was then used to train an autoencoder in an semi-supervised approach. The resultant autoencoder was able to indentify normal observations. A subsequent data set was then analysed by the autoencoder. The resultant reconstruction errors were then compared to the ground truth events to investigate the effectiveness of the autoencoder’s anomaly detection capability. DA - 2019 DB - OpenUCT DP - University of Cape Town KW - Statistics LK - https://open.uct.ac.za PY - 2019 T1 - Anomaly detection in a mobile data network TI - Anomaly detection in a mobile data network UR - http://hdl.handle.net/11427/31202 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11427/31202 | |
| dc.identifier.vancouvercitation | Salzwedel JP. Anomaly detection in a mobile data network. []. ,Faculty of Science ,Department of Statistical Sciences, 2019 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/31202 | en_ZA |
| dc.language.rfc3066 | eng | |
| dc.publisher.department | Department of Statistical Sciences | |
| dc.publisher.faculty | Faculty of Science | |
| dc.subject | Statistics | |
| dc.title | Anomaly detection in a mobile data network | |
| dc.type | Master Thesis | |
| dc.type.qualificationlevel | Masters | |
| dc.type.qualificationname | MSc |