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March 3, 2026
ADAP-GNN: Adaptive property-aware graph neural network for intrusion detection in IoT networks
MT
Mortada Termos
Lebanese University
ZG
Zakariya Ghalmane
Cole Engineering Services (United States)
MZ
Mourad Zghal
Cole Engineering Services (United States)
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Key Points
ADAP-GNN improves intrusion detection performance in IoT networks, enhancing security measures.
The model achieves up to a 20% increase in detection accuracy compared to traditional methods.
Application of adaptive algorithms within a machine learning framework boosts detection rates significantly.
This approach supports real-time threat identification, highlighting the need for advanced security in IoT ecosystems.
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Cite This Study
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Termos et al. (Tue,) studied this question.
synapsesocial.com/papers/69a7618ec6e9836116a2f95a
https://doi.org/https://doi.org/10.1016/j.compeleceng.2026.111051
ADAP-GNN: Adaptive property-aware graph neural network for intrusion detection in IoT networks | Synapse