A Survey on Malware Detection with Deep Learning

Muhammet Sahin, Serif Bahtiyar

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Citations (Scopus)

Abstract

Rapid development of Internet and technology has emerged a bunch of evolving malware and attack strategies. Therefore researchers focused on machine learning and deep learning methods to detect malware (viruses, bots, ransomware, trojans). In order to protect users from this treats many companies have been developing new algorithms and products. However, malware types have been increasing dramatically. Anti-malware producers have been detecting with millions of new malware types each year. So in order to stop that increase, there is an urgent need to develop new intelligent methods on malware detection. In this work, we have overviewed current intelligent machine learning and deep learning methods to solve malware detection. In this sense, we will present malware feature extraction and classification methods. Also, we will discuss more issues and challenges on that problem. Finally, we will share our foresight on malware detection methods.

Original languageEnglish
Title of host publicationProceedings of the 13th International Conference on Security of Information and Networks, SIN 2020
EditorsBerna Ors, Atilla Elci
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450387514
DOIs
Publication statusPublished - 4 Nov 2020
Event13th International Conference on Security of Information and Networks, SIN 2020 - Virtual, Online, Turkey
Duration: 4 Nov 20206 Nov 2020

Publication series

NameACM International Conference Proceeding Series

Conference

Conference13th International Conference on Security of Information and Networks, SIN 2020
Country/TerritoryTurkey
CityVirtual, Online
Period4/11/206/11/20

Bibliographical note

Publisher Copyright:
© 2020 ACM.

Keywords

  • Classification
  • Deep Learning
  • Detection
  • Malware

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