Show simple item record

dc.contributor.authorGibert Llauradó, Daniel
dc.contributor.authorMateu Piñol, Carles
dc.contributor.authorPlanes Cid, Jordi
dc.date.accessioned2020-03-26T09:32:16Z
dc.date.available2020-03-26T09:32:16Z
dc.date.issued2020
dc.identifier.issn1084-8045
dc.identifier.urihttp://hdl.handle.net/10459.1/68344
dc.description.abstractThe struggle between security analysts and malware developers is a never-ending battle with the complexity of malware changing as quickly as innovation grows. Current state-of-the-art research focus on the development and application of machine learning techniques for malware detection due to its ability to keep pace with malware evolution. This survey aims at providing a systematic and detailed overview of machine learning techniques for malware detection and in particular, deep learning techniques. The main contributions of the paper are: (1) it provides a complete description of the methods and features in a traditional machine learning workflow for malware detection and classification, (2) it explores the challenges and limitations of traditional machine learning and (3) it analyzes recent trends and developments in the field with special emphasis on deep learning approaches. Furthermore, (4) it presents the research issues and unsolved challenges of the state-of-the-art techniques and (5) it discusses the new directions of research. The survey helps researchers to have an understanding of the malware detection field and of the new developments and directions of research explored by the scientific community to tackle the problem.ca_ES
dc.description.sponsorshipThis research has been partially funded by the Spanish MICINN Projects TIN2015-71799-C2-2-P, ENE2015-64117-C5-1-R, and is supported by the University of Lleida. This research article has received a grant (2019 call) from the University of Lleida Language Institute to review the English.ca_ES
dc.language.isoengca_ES
dc.publisherElsevierca_ES
dc.relationMINECO/PN2013-2016/TIN2015-71799-C2-2-Pca_ES
dc.relationMINECO/PN2013-2016/ENE2015-64117-C5-1-Rca_ES
dc.relation.isformatofReproducció del document publicat a https://doi.org/10.1016/j.jnca.2019.102526ca_ES
dc.relation.ispartofJournal of Network and Computer Applications, 2020, vol. 153, 102526ca_ES
dc.rightscc-by-nc-nd (c) Gibert et al., 2020ca_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectMalware detectionca_ES
dc.subjectFeature engineeringca_ES
dc.subjectMachine learningca_ES
dc.titleThe rise of machine learning for detection and classification of malware: Research developments, trends and challengeca_ES
dc.typeinfo:eu-repo/semantics/articleca_ES
dc.identifier.idgrec030101
dc.type.versioninfo:eu-repo/semantics/publishedVersionca_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca_ES
dc.identifier.doihttps://doi.org/10.1016/j.jnca.2019.102526


Files in this item

Thumbnail
Thumbnail

This item appears in the following Collection(s)

Show simple item record

cc-by-nc-nd (c) Gibert et al., 2020
Except where otherwise noted, this item's license is described as cc-by-nc-nd (c) Gibert et al., 2020