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Spamming mobile botnet detection using computational intelligence
Dissertation (MSc)--University of Pretoria, 2013.
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| Other Authors: | |
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2014
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| _version_ | 1867613671784448000 |
|---|---|
| access_status_str | Open Access |
| author2 | Venter, Hein S. |
| author_browse | Venter, Hein S. |
| author_facet | Venter, Hein S. |
| collection | Thesis |
| dc_rights_str_mv | © 2013 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Dissertation (MSc)--University of Pretoria, 2013. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/36775 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:39:51.634Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2014 |
| publishDateRange | 2014 |
| publishDateSort | 2014 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/36775 Spamming mobile botnet detection using computational intelligence Venter, Hein S. ickin.vural@gmail.com Vural, Ickin Spam Malware Bot Botnet Mobile Computational intelligence Artificial immune system Bayesian spam filtering Neural networks UCTD Dissertation (MSc)--University of Pretoria, 2013. This dissertation explores a new challenge to digital systems posed by the adaptation of mobile devices and proposes a countermeasure to secure systems against threats to this new digital ecosystem. The study provides the reader with background on the topics of spam, Botnets and machine learning before tackling the issue of mobile spam. The study presents the reader with a three tier model that uses machine learning techniques to combat spamming mobile Botnets. The three tier model is then developed into a prototype and demonstrated to the reader using test scenarios. Finally, this dissertation critically discusses the advantages of having using the three tier model to combat spamming Botnets. gm2014 Computer Science unrestricted 2014-02-26T11:16:55Z 2014-02-26T11:16:55Z 2013-09-04 2013 Dissertation Vural, I 2013, Spamming mobile botnet detection using computational intelligence, MSc dissertation, University of Pretoria, Pretoria, viewed yymmdd <http://hdl.handle.net/2263/36775> E13/9/1142/gm http://hdl.handle.net/2263/36775 en © 2013 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria |
| spellingShingle | Spam Malware Bot Botnet Mobile Computational intelligence Artificial immune system Bayesian spam filtering Neural networks UCTD Spamming mobile botnet detection using computational intelligence |
| title | Spamming mobile botnet detection using computational intelligence |
| title_full | Spamming mobile botnet detection using computational intelligence |
| title_fullStr | Spamming mobile botnet detection using computational intelligence |
| title_full_unstemmed | Spamming mobile botnet detection using computational intelligence |
| title_short | Spamming mobile botnet detection using computational intelligence |
| title_sort | spamming mobile botnet detection using computational intelligence |
| topic | Spam Malware Bot Botnet Mobile Computational intelligence Artificial immune system Bayesian spam filtering Neural networks UCTD |
| url | http://hdl.handle.net/2263/36775 |