Systematic analysis on mobile botnet detection techniques using genetic algorithm

Nowadays smart phone has been used all over the world and has become as one of the most targeted platforms of mobile botnet to steal confidential information especially related with online banking. It is seen as one of the most dangerous cyber threat. Therefore in this research paper, a systematic a...

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Main Authors: M.Z.A., Rahman, M.M., Saudi
Format: Conference Paper
Language:en_US
Published: Springer Verlag 2015
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Online Access:http://ddms.usim.edu.my/handle/123456789/9245
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Institution: Universiti Sains Islam Malaysia
Language: en_US
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spelling my.usim-92452015-08-26T04:02:36Z Systematic analysis on mobile botnet detection techniques using genetic algorithm M.Z.A., Rahman M.M., Saudi Android Genetic algorithm Mobile botnet Reverse engineering Nowadays smart phone has been used all over the world and has become as one of the most targeted platforms of mobile botnet to steal confidential information especially related with online banking. It is seen as one of the most dangerous cyber threat. Therefore in this research paper, a systematic analysis on mobile botnet detection techniques is further investigated and evaluated. A case study was carried out to reverse engineering the mobile botnet codes. Based on the findings, this mobile botnet has successfully posed itself as a fake anti-virus and has the capability to steal important data such as username and password from the Android-based devices. Furthermore, this paper also discusses the challenges and the potential research for future work with relate of the genetic algorithm. This research paper can be used as a reference and guidance for further study on mobile botnet detection techniques. 2015-08-26T04:02:36Z 2015-08-26T04:02:36Z 2015-01-01 Conference Paper 9783-3190-7673-7 1876-1100 http://ddms.usim.edu.my/handle/123456789/9245 en_US Springer Verlag
institution Universiti Sains Islam Malaysia
building USIM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universit Sains Islam i Malaysia
content_source USIM Institutional Repository
url_provider http://ddms.usim.edu.my/
language en_US
topic Android
Genetic algorithm
Mobile botnet
Reverse engineering
spellingShingle Android
Genetic algorithm
Mobile botnet
Reverse engineering
M.Z.A., Rahman
M.M., Saudi
Systematic analysis on mobile botnet detection techniques using genetic algorithm
description Nowadays smart phone has been used all over the world and has become as one of the most targeted platforms of mobile botnet to steal confidential information especially related with online banking. It is seen as one of the most dangerous cyber threat. Therefore in this research paper, a systematic analysis on mobile botnet detection techniques is further investigated and evaluated. A case study was carried out to reverse engineering the mobile botnet codes. Based on the findings, this mobile botnet has successfully posed itself as a fake anti-virus and has the capability to steal important data such as username and password from the Android-based devices. Furthermore, this paper also discusses the challenges and the potential research for future work with relate of the genetic algorithm. This research paper can be used as a reference and guidance for further study on mobile botnet detection techniques.
format Conference Paper
author M.Z.A., Rahman
M.M., Saudi
author_facet M.Z.A., Rahman
M.M., Saudi
author_sort M.Z.A., Rahman
title Systematic analysis on mobile botnet detection techniques using genetic algorithm
title_short Systematic analysis on mobile botnet detection techniques using genetic algorithm
title_full Systematic analysis on mobile botnet detection techniques using genetic algorithm
title_fullStr Systematic analysis on mobile botnet detection techniques using genetic algorithm
title_full_unstemmed Systematic analysis on mobile botnet detection techniques using genetic algorithm
title_sort systematic analysis on mobile botnet detection techniques using genetic algorithm
publisher Springer Verlag
publishDate 2015
url http://ddms.usim.edu.my/handle/123456789/9245
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