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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Veröffentlicht in:	PeerJ. Computer science Jg. 8; S. e988
1. Verfasser:	Gunduz, Hakan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States PeerJ. Ltd 18.05.2022 PeerJ, Inc PeerJ Inc
Schlagworte:	API-call graphs Application programming interface Artificial Intelligence Data Mining and Machine Learning Feature extraction Feature selection Graph embeddings Graph Variational Autoencoder Graphs Machine learning Malware Malware detection Mobile and Ubiquitous Computing Model accuracy Neural Networks Recursive Feature Elimination Security and Privacy Spyware API-call graphs Graph embeddings Recursive Feature Elimination Malware detection Graph Variational Autoencoder
ISSN:	2376-5992, 2376-5992
Online-Zugang:	Volltext
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