Evading control flow graph based GNN malware detectors via active opcode insertion method with malic...
Evading control flow graph based GNN malware detectors via active opcode insertion method with maliciousness preserving
About this item
Full title
Author / Creator
Peng, Hao , Yu, Zehao , Zhao, Dandan , Ding, Zhiguo , Yang, Jieshuai , Zhang, Bo , Han, Jianming , Zhang, Xuhong , Ji, Shouling and Zhong, Ming
Publisher
London: Nature Publishing Group UK
Journal title
Language
English
Formats
Publication information
Publisher
London: Nature Publishing Group UK
Subjects
More information
Scope and Contents
Contents
With the continuous advancement of machine learning, numerous malware detection methods that leverage this technology have emerged, presenting new challenges to the generation of adversarial malware. Existing function-preserving adversarial attacks fall short of effectively modifying portable executable (PE) malware control flow graphs (CFGs), ther...
Alternative Titles
Full title
Evading control flow graph based GNN malware detectors via active opcode insertion method with maliciousness preserving
Authors, Artists and Contributors
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Primary Identifiers
Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_d1220d2509fb41e3a1cc36b94a9734fa
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_d1220d2509fb41e3a1cc36b94a9734fa
Other Identifiers
ISSN
2045-2322
E-ISSN
2045-2322
DOI
10.1038/s41598-025-92023-7
