Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/19006
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dc.contributor.authorSalman Jan-
dc.contributor.authorShahrulniza Musa-
dc.contributor.authorToqeer Ali-
dc.contributor.authorAli Alzahrani-
dc.date.accessioned2018-07-09T08:32:01Z-
dc.date.available2018-07-09T08:32:01Z-
dc.date.issued2018-07-09-
dc.identifier.urihttp://ir.unikl.edu.my/jspui/handle/123456789/19006-
dc.descriptionVenue : Malaysian Institute of Information Technology, Universiti Kuala Lumpur, Kuala Lumpur, Malaysiaen_US
dc.description.abstractMalware analysis for Android systems has been the focus of considerable research in the past few years due to the large customer base moving towards Android, which has attracted a corresponding number of malware writers. Several techniques have been used to detect the malicious behavior of Android applications as well as that of the complete system. Machine-learning techniques have been used in the past to assess the behavior of an application using either static or dynamic analysisen_US
dc.subjectAndroid securityen_US
dc.subjectMalware detectionen_US
dc.subjectDeep Learningen_US
dc.subjectDCGANen_US
dc.titleDeep Convolutional Generative Adversarial Networks for Intent-based Dynamic Behavior Captureen_US
dc.typeArticleen_US
dc.conference.nameInternational Conference on Information and Communication Technology (ICICTM)en_US
dc.conference.year2018en_US
Appears in Collections:Conference Paper

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