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Anomaly detection in a mobile data network
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 autoe...
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| Other Authors: | |
| Format: | Thesis |
| Language: | English |
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Department of Statistical Sciences
2020
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| _version_ | 1867614171571421184 |
|---|---|
| access_status_str | Open Access |
| author | Salzwedel, Jason Paul |
| author2 | Ngwenya, Mzabalazo |
| author_browse | Ngwenya, Mzabalazo Salzwedel, Jason Paul |
| author_facet | Ngwenya, Mzabalazo Salzwedel, Jason Paul |
| author_sort | Salzwedel, Jason Paul |
| collection | Thesis |
| description | 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. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/31202 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:47:48.384Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2020 |
| publishDateRange | 2020 |
| publishDateSort | 2020 |
| publisher | Department of Statistical Sciences |
| publisherStr | Department of Statistical Sciences |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/31202 Anomaly detection in a mobile data network Salzwedel, Jason Paul Ngwenya, Mzabalazo Statistics 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. 2020-02-20T11:10:31Z 2020-02-20T11:10:31Z 2019 2020-02-14T12:21:35Z Master Thesis Masters MSc http://hdl.handle.net/11427/31202 eng application/pdf Department of Statistical Sciences Faculty of Science |
| spellingShingle | Statistics Salzwedel, Jason Paul Anomaly detection in a mobile data network |
| thesis_degree_str | Master's |
| title | Anomaly detection in a mobile data network |
| title_full | Anomaly detection in a mobile data network |
| title_fullStr | Anomaly detection in a mobile data network |
| title_full_unstemmed | Anomaly detection in a mobile data network |
| title_short | Anomaly detection in a mobile data network |
| title_sort | anomaly detection in a mobile data network |
| topic | Statistics |
| url | http://hdl.handle.net/11427/31202 |
| work_keys_str_mv | AT salzwedeljasonpaul anomalydetectioninamobiledatanetwork |