The rise of machine learning for detection and classification of malware: Research developments, trends and challenge

The 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 ab...

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Detalles Bibliográficos
Autores: Gibert Llauradó, Daniel, Mateu Piñol, Carles, Planes Cid, Jordi
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2020
País:España
Institución:Universitat de Lleida (UdL)
Repositorio:Repositori Obert UdL
OAI Identifier:oai:repositori.udl.cat:10459.1/68344
Acceso en línea:https://doi.org/10.1016/j.jnca.2019.102526
http://hdl.handle.net/10459.1/68344
Access Level:acceso abierto
Palabra clave:Malware detection
Feature engineering
Machine learning
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spelling The rise of machine learning for detection and classification of malware: Research developments, trends and challengeGibert Llauradó, DanielMateu Piñol, CarlesPlanes Cid, JordiMalware detectionFeature engineeringMachine learningThe 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.This 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.Elsevier2020info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://doi.org/10.1016/j.jnca.2019.102526http://hdl.handle.net/10459.1/68344reponame:Repositori Obert UdL instname:Universitat de Lleida (UdL)Inglésinfo:eu-repo/grantAgreement/MINECO//TIN2015-71799-C2-2-Pinfo:eu-repo/grantAgreement/MINECO//ENE2015-64117-C5-1-RReproducció del document publicat a https://doi.org/10.1016/j.jnca.2019.102526Journal of Network and Computer Applications, 2020, vol. 153, 102526cc-by-nc-nd (c) Gibert et al., 2020info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-nd/4.0/oai:repositori.udl.cat:10459.1/683442026-06-24T12:42:17Z
dc.title.none.fl_str_mv The rise of machine learning for detection and classification of malware: Research developments, trends and challenge
title The rise of machine learning for detection and classification of malware: Research developments, trends and challenge
spellingShingle The rise of machine learning for detection and classification of malware: Research developments, trends and challenge
Gibert Llauradó, Daniel
Malware detection
Feature engineering
Machine learning
title_short The rise of machine learning for detection and classification of malware: Research developments, trends and challenge
title_full The rise of machine learning for detection and classification of malware: Research developments, trends and challenge
title_fullStr The rise of machine learning for detection and classification of malware: Research developments, trends and challenge
title_full_unstemmed The rise of machine learning for detection and classification of malware: Research developments, trends and challenge
title_sort The rise of machine learning for detection and classification of malware: Research developments, trends and challenge
dc.creator.none.fl_str_mv Gibert Llauradó, Daniel
Mateu Piñol, Carles
Planes Cid, Jordi
author Gibert Llauradó, Daniel
author_facet Gibert Llauradó, Daniel
Mateu Piñol, Carles
Planes Cid, Jordi
author_role author
author2 Mateu Piñol, Carles
Planes Cid, Jordi
author2_role author
author
dc.subject.none.fl_str_mv Malware detection
Feature engineering
Machine learning
topic Malware detection
Feature engineering
Machine learning
description The 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.
publishDate 2020
dc.date.none.fl_str_mv 2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://doi.org/10.1016/j.jnca.2019.102526
http://hdl.handle.net/10459.1/68344
url https://doi.org/10.1016/j.jnca.2019.102526
http://hdl.handle.net/10459.1/68344
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/grantAgreement/MINECO//TIN2015-71799-C2-2-P
info:eu-repo/grantAgreement/MINECO//ENE2015-64117-C5-1-R
Reproducció del document publicat a https://doi.org/10.1016/j.jnca.2019.102526
Journal of Network and Computer Applications, 2020, vol. 153, 102526
dc.rights.none.fl_str_mv cc-by-nc-nd (c) Gibert et al., 2020
info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-nd/4.0/
rights_invalid_str_mv cc-by-nc-nd (c) Gibert et al., 2020
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Repositori Obert UdL
instname:Universitat de Lleida (UdL)
instname_str Universitat de Lleida (UdL)
reponame_str Repositori Obert UdL
collection Repositori Obert UdL
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repository.mail.fl_str_mv
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