Spamming mobile botnet detection using computational intelligence
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University of Pretoria
Abstract
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.
Description
Dissertation (MSc)--University of Pretoria, 2013.
Keywords
Spam, Malware, Bot, Botnet, Mobile, Computational intelligence, Artificial immune system, Bayesian spam filtering, Neural networks, UCTD
Sustainable Development Goals
Citation
Vural, I 2013, Spamming mobile botnet detection using computational intelligence, MSc dissertation, University of Pretoria, Pretoria, viewed yymmdd <http://hdl.handle.net/2263/36775>