Please use this identifier to cite or link to this item: http://ir.unikl.edu.my/jspui/handle/123456789/19006
metadata.conference.dc.title: Deep Convolutional Generative Adversarial Networks for Intent-based Dynamic Behavior Capture
metadata.conference.dc.contributor.*: Salman Jan
Shahrulniza Musa
Toqeer Ali
Ali Alzahrani
metadata.conference.dc.subject: Android security
Malware detection
Deep Learning
DCGAN
metadata.conference.dc.date.issued: 9-Jul-2018
metadata.conference.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
metadata.conference.dc.description: Venue : Malaysian Institute of Information Technology, Universiti Kuala Lumpur, Kuala Lumpur, Malaysia
metadata.conference.dc.identifier.uri: http://ir.unikl.edu.my/jspui/handle/123456789/19006
metadata.conference.dc.conference.name: International Conference on Information and Communication Technology (ICICTM)
metadata.conference.dc.conference.year: 2018
Appears in Collections:Conference Papers

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