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Detection of mobile botnets using neural networks

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dc.title Detection of mobile botnets using neural networks en
dc.contributor.author Oulehla, Milan
dc.contributor.author Komínková Oplatková, Zuzana
dc.contributor.author Malaník, David
dc.relation.ispartof FTC 2016 - Proceedings of Future Technologies Conference
dc.identifier.isbn 9781509041718
dc.date.issued 2016
dc.citation.spage 1324
dc.citation.epage 1326
dc.event.title 2016 Future Technologies Conference, FTC 2016
dc.event.location San Francisco
utb.event.state-en United States
utb.event.state-cs Spojené státy americké
dc.event.sdate 2016-12-06
dc.event.edate 2016-12-07
dc.type conferenceObject
dc.language.iso en
dc.publisher Institute of Electrical and Electronics Engineers (IEEE)
dc.identifier.doi 10.1109/FTC.2016.7821774
dc.relation.uri http://ieeexplore.ieee.org/document/7821774/
dc.subject Android en
dc.subject bot en
dc.subject information gain en
dc.subject mobile botnet en
dc.subject mobile malware en
dc.subject neural networks en
dc.subject Ranker algorithm en
dc.description.abstract This poster deals with botnets, the most dangerous kind of mobile malware, and their detection using neural networks. Unlike common mobile malware, botnets often have a complicated pattern of behavior because they are not managed by predictable algorithms but they are controlled by humans via command and control servers (C&C servers) or via peer-to-peer networks. However, they have certain common features which have been revealed by analysis of contemporary mobile botnets. These features have been used for creation of a neural network training set. Finally, the design of parallel architecture using neural network for useful detection of mobile botnets has been described. © 2016 IEEE. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1007114
utb.identifier.obdid 43876373
utb.identifier.scopus 2-s2.0-85013664701
utb.identifier.wok 000399455300189
utb.source d-wok
dc.date.accessioned 2017-08-01T08:27:12Z
dc.date.available 2017-08-01T08:27:12Z
dc.description.sponsorship ERDF, European Regional Development Fund
dc.description.sponsorship Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme [LO1303 (MSMT-7778/2014)]; European Regional Development Fund under the project CEBIA-Tech [CZ.1.05/2.1.00/03.0089]; Grant Agency of the Czech Republic-GACR [588 P103/15/06700S]; Internal Grant Agency of Tomas Bata University in Zlin [IGA/FAI/2016/016]
utb.contributor.internalauthor Oulehla, Milan
utb.contributor.internalauthor Komínková Oplatková, Zuzana
utb.contributor.internalauthor Malaník, David
utb.fulltext.affiliation Milan Oulehla Faculty of Applied Informatics Tomas Bata University in Zlin Zlin, Czech Republic oulehla@fai.utb.cz Zuzana Komínková Oplatková Faculty of Applied Informatics Tomas Bata University in Zlin Zlin, Czech Republic kominkovaoplatkova@fai.utb.cz David Malanik Faculty of Applied Informatics Tomas Bata University in Zlin Zlin, Czech Republic dmalanik@fai.utb.cz
utb.fulltext.dates -
utb.fulltext.sponsorship This work was supported by the Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme project No. LO1303 (MSMT-7778/2014) and also by the European Regional Development Fund under the project CEBIA-Tech No. CZ.1.05/2.1.00/03.0089, further it was supported by Grant Agency of the Czech Republic—GACR 588 P103/15/06700S and by Internal Grant Agency of Tomas Bata University in Zlin under the project No. IGA/FAI/2016/016.
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