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Mobile botnet detection model based on retrospective pattern recognition

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dc.citedby7
dc.contributor.authorEslahi M.en_US
dc.contributor.authorYousefi M.en_US
dc.contributor.authorNaseri M.V.en_US
dc.contributor.authorYussof Y.M.en_US
dc.contributor.authorTahir N.M.en_US
dc.contributor.authorHashim H.en_US
dc.contributor.authorid55639528700en_US
dc.contributor.authorid53985756300en_US
dc.contributor.authorid56463729100en_US
dc.contributor.authorid35093577700en_US
dc.contributor.authorid56168849900en_US
dc.contributor.authorid16021805400en_US
dc.date.accessioned2023-05-29T06:13:31Z
dc.date.available2023-05-29T06:13:31Z
dc.date.issued2016
dc.descriptionComplex networks; HTTP; Hypertext systems; Mobile security; Pattern recognition; Botnet detections; Botnets; BYOD; Command and control; Experimental test; Network-based modeling; Packet payloads; Traffic analysis; Malwareen_US
dc.description.abstractThe dynamic nature of Botnets along with their sophisticated characteristics makes them one of the biggest threats to cyber security. Recently, the HTTP protocol is widely used by Botmaster as they can easily hide their command and control traffic amongst the benign web traffic. This paper proposes a Neural Network based model to detect mobile HTTP Botnets with random intervals independent of the packet payload, commands content, and encryption complexity of Bot communications. The experimental test results that were conducted on existing datasets and real world Bot samples show that the proposed method is able to detect mobile HTTP Botnets with high accuracy. � 2016 SERSC.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.14257/ijsia.2016.10.9.05
dc.identifier.epage54
dc.identifier.issue9
dc.identifier.scopus2-s2.0-84992073868
dc.identifier.spage39
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84992073868&doi=10.14257%2fijsia.2016.10.9.05&partnerID=40&md5=a3af90bfdfc2888cac26e2fc943f9c03
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/22937
dc.identifier.volume10
dc.publisherScience and Engineering Research Support Societyen_US
dc.relation.ispartofAll Open Access, Bronze
dc.sourceScopus
dc.sourcetitleInternational Journal of Security and its Applications
dc.titleMobile botnet detection model based on retrospective pattern recognitionen_US
dc.typeArticleen_US
dspace.entity.typePublication
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