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February 17, 2026Computers & Electrical Engineering

ADAP-GNN: Adaptive property-aware graph neural network for intrusion detection in IoT networks

Mortada Termos Zakariya Ghalmane Mourad Zghal Ahmad Fadlallah Ali Jaber Mourad Zghal

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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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Implication

The proposed approach detects intrusions in IoT networks, suggesting effective use of graph neural networks for enhanced security.

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Cite This Study

Termos et al. (2026) studied this question.

synapsesocial.com/papers/69a7618ec6e9836116a2f95a https://doi.org/10.1016/j.compeleceng.2026.111051

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