dc.contributor.author | Catak, Ferhat Özgur | |
dc.contributor.author | Yazi, Ahmet Faruk | |
dc.contributor.author | Gul, Ensar | |
dc.date.accessioned | 2020-04-03T10:22:14Z | |
dc.date.available | 2020-04-03T10:22:14Z | |
dc.date.created | 2020-01-17T11:18:09Z | |
dc.date.issued | 2019 | |
dc.identifier.isbn | 978-1-7281-1904-5 | |
dc.identifier.uri | https://hdl.handle.net/11250/2650279 | |
dc.description.abstract | Nowadays, anti-virus applications using traditional signature-based detection methods fail to detect metamorphic malware. For this reason, recent studies on the detection and classification of malicious software address the behavior of malware. In this study, an LSTM based classification method was developed by using API calls of 8 different types of real malware. With this method, the behaviors of the malware types on the operating system are modeled. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_US |
dc.relation.ispartof | IEEE Conference Publication | |
dc.relation.ispartofseries | IEEE Conference Publication; | |
dc.title | Classification of Methamorphic Malware with Deep Learning (LSTM) | en_US |
dc.type | Chapter | en_US |
dc.description.version | acceptedVersion | en_US |
dc.source.pagenumber | 4 | en_US |
dc.identifier.doi | 10.1109/SIU.2019.8806571 | |
dc.identifier.cristin | 1775608 | |
dc.description.localcode | © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en_US |
cristin.unitcode | 194,63,30,0 | |
cristin.unitname | Institutt for informasjonssikkerhet og kommunikasjonsteknologi | |
cristin.ispublished | true | |
cristin.fulltext | postprint | |
cristin.qualitycode | 1 | |