dc.identifier.uri |
http://dx.doi.org/10.15488/13913 |
|
dc.identifier.uri |
https://www.repo.uni-hannover.de/handle/123456789/14027 |
|
dc.contributor.author |
Saidia Fascí, Lara
|
|
dc.contributor.author |
Fisichella, Marco
|
|
dc.contributor.author |
Lax, Gianluca
|
|
dc.contributor.author |
Qian, Chenyi
|
|
dc.date.accessioned |
2023-06-23T06:48:31Z |
|
dc.date.available |
2023-06-23T06:48:31Z |
|
dc.date.issued |
2022 |
|
dc.identifier.citation |
Saidia Fascí, L.; Fisichella, M.; Lax, G.; Qian, C.: Disarming visualization-based approaches in malware detection systems. In: Computers & Security 126 (2023), 103062. DOI: https://doi.org/10.1016/j.cose.2022.103062 |
|
dc.description.abstract |
Visualization-based approaches have recently been used in conjunction with signature-based techniques to detect variants of malware files. Indeed, it is sufficient to modify some byte of executable files to modify the signature and, thus, to elude a signature-based detector. In this paper, we design a GAN-based architecture that allows an attacker to generate variants of a malware in which the malware patterns found by visualization-based approaches are hidden, thus producing a new version of the malware that is not detected by both signature-based and visualization-based techniques. The experiments carried out on a well-known malware dataset show a success rate of 100% in generating new variants of malware files that are not detected from the state-of-the-art visualization-based technique. |
eng |
dc.language.iso |
eng |
|
dc.publisher |
Amsterdam [u.a.] : Elsevier Science |
|
dc.relation.ispartofseries |
Computers & Security 126 (2023) |
|
dc.rights |
CC BY 4.0 Unported |
|
dc.rights.uri |
https://creativecommons.org/licenses/by/4.0 |
|
dc.subject |
Deep learning |
eng |
dc.subject |
GAN |
eng |
dc.subject |
Machine learning |
eng |
dc.subject |
Malware classification |
eng |
dc.subject.ddc |
004 | Informatik
|
ger |
dc.title |
Disarming visualization-based approaches in malware detection systems |
eng |
dc.type |
Article |
|
dc.type |
Text |
|
dc.relation.essn |
0167-4048 |
|
dc.relation.issn |
0167-4048 |
|
dc.relation.doi |
https://doi.org/10.1016/j.cose.2022.103062 |
|
dc.bibliographicCitation.volume |
126 |
|
dc.bibliographicCitation.date |
2023 |
|
dc.bibliographicCitation.firstPage |
103062 |
|
dc.description.version |
publishedVersion |
|
tib.accessRights |
frei zug�nglich |
|