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

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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Bibliographic Details
Main Author: Hakan Gunduz
Format: Article
Language:English
Published: PeerJ Inc. 2022-05-01
Series:PeerJ Computer Science
Subjects:
Malware detection
Recursive Feature Elimination
Graph Variational Autoencoder
Graph embeddings
API-call graphs
Online Access:https://peerj.com/articles/cs-988.pdf

Internet

https://peerj.com/articles/cs-988.pdf

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