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Deep Convolutional Generative Adversarial Networks for Intent-based Dynamic Behavior Capture

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dc.contributor.author Salman Jan
dc.contributor.author Shahrulniza Musa
dc.contributor.author Toqeer Ali
dc.contributor.author Ali Alzahrani
dc.date.accessioned 2018-07-09T08:32:01Z
dc.date.available 2018-07-09T08:32:01Z
dc.date.issued 2018-07-09
dc.identifier.uri http://ir.unikl.edu.my/jspui/handle/123456789/19006
dc.description Venue : Malaysian Institute of Information Technology, Universiti Kuala Lumpur, Kuala Lumpur, Malaysia en_US
dc.description.abstract Malware 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 analysis en_US
dc.subject Android security en_US
dc.subject Malware detection en_US
dc.subject Deep Learning en_US
dc.subject DCGAN en_US
dc.title Deep Convolutional Generative Adversarial Networks for Intent-based Dynamic Behavior Capture en_US
dc.type Article en_US
dc.conference.name International Conference on Information and Communication Technology (ICICTM) en_US
dc.conference.year 2018 en_US


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