Variational Graph Convolutional Networks for Dynamic Graph Representation Learning

The ubiquitous and ever-evolving nature of cyber threats demands innovative approaches that can adapt to the dynamic relationships and structures within network data. Traditional models struggle to adapt to the constantly changing nature of network traffic, where both structural dependencies and tem...

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Veröffentlicht in:	IEEE access Jg. 12; S. 161697 - 161717
Hauptverfasser:	Mir, Aabid A., Zuhairi, Megat F., Musa, Shahrulniza, Alanazi, Meshari H., Namoun, Abdallah
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Accuracy Adaptation models Analytical models Anomaly detection Artificial neural networks Classification Communications traffic complex networks Computer security Convolution Cybersecurity Datasets deep learning Dynamic structural analysis Graph convolutional networks graph neural networks Graph representations Graphical representations heterogeneous networks Machine learning Mathematical models Modelling Network analysis network security Nodes Performance evaluation Predictive models Probabilistic inference Probabilistic logic Probabilistic models Social networks Telecommunication traffic Traffic analysis Uncertainty
ISSN:	2169-3536, 2169-3536
Online-Zugang:	Volltext
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