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dc.contributor.authorMcLaughlin, Niallen
dc.contributor.authorMartinez del Rincon, Jesusen
dc.contributor.authorKang, BooJoongen
dc.contributor.authorYerima, Suleimanen
dc.contributor.authorMiller, Paulen
dc.contributor.authorSezer, Sakiren
dc.contributor.authorSafaei, Yeganehen
dc.contributor.authorTrickel, Eriken
dc.contributor.authorZhao, Zimingen
dc.contributor.authorDoupe, Adamen
dc.contributor.authorGail Joon Ahnen
dc.date.accessioned2018-10-31T13:33:22Z
dc.date.available2018-10-31T13:33:22Z
dc.date.issued2017-03
dc.identifier.citationMcLaughlin, N., Martinez del Rincon, J., Kang, B., Yerima, S., Miller, P., Sezer, S., ... Joon Ahn, G. (2017). Deep Android Malware Detection. In Proceedings of the ACM Conference on Data and Applications Security and Privacy (CODASPY) 2017 Association for Computing Machinery (ACM).en
dc.identifier.isbn978-1-4503-4523-1
dc.identifier.urihttp://hdl.handle.net/2086/16947
dc.description.abstractIn this paper, we propose a novel android malware detection system that uses a deep convolutional neural network (CNN). Malware classification is performed based on static analysis of the raw opcode sequence from a disassembled program. Features indicative of malware are automatically learned by the network from the raw opcode sequence thus removing the need for hand-engineered malware features. The training pipeline of our proposed system is much simpler than existing n-gram based malware detection methods, as the network is trained end-to-end to jointly learn appropriate features and to perform classification, thus removing the need to explicitly enumerate millions of n-grams during training. The network design also allows the use of long n-gram like features, not computationally feasible with existing methods. Once trained, the network can be efficiently executed on a GPU, allowing a very large number of files to be scanned quickly.en
dc.language.isoenen
dc.publisherACMen
dc.subjectmachine learningen
dc.subjectneural networksen
dc.subjectconvolutional neural networksen
dc.subjectandroid malwareen
dc.subjectmalware detectionen
dc.subjectopcodesen
dc.subjectn-gramsen
dc.titleDeep android malware detectionen
dc.typeConferenceen
dc.identifier.doihttps://doi.org/10.1145/3029806.3029823
dc.peerreviewedYesen
dc.funderN/Aen
dc.projectidN//aen
dc.cclicenceCC-BY-NCen
dc.date.acceptance2017en
dc.researchinstituteCyber Technology Institute (CTI)en


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