Malware detection framework based on graph variational autoencoder extracted embeddings from API-call graphs

Malware harms the confidentiality and integrity of the information that causes material and moral damages to institutions or individuals. This study proposed a malware detection model based on API-call graphs and used Graph Variational Autoencoder (GVAE) to reduce the size of graph node features ext...

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Detalles Bibliográficos
Autor principal: Gunduz, Hakan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2022
Materias:
Artificial Intelligence
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9137949/
https://www.ncbi.nlm.nih.gov/pubmed/35634097
http://dx.doi.org/10.7717/peerj-cs.988

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9137949/
https://www.ncbi.nlm.nih.gov/pubmed/35634097
http://dx.doi.org/10.7717/peerj-cs.988

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