ExAI5G combines Transformer-based intrusion detection with surrogate decision trees and LLM-evaluated explanations to deliver 99.9% accuracy and 16 high-fidelity logical rules on 5G IoT traffic while preserving performance.
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Heterogeneous graph neural networks with post-hoc explanations improve accuracy on six land-use indicators from mobility data and provide feature attribution and counterfactual insights aligned with commuting patterns.
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ExAI5G: A Logic-Based Explainable AI Framework for Intrusion Detection in 5G Networks
ExAI5G combines Transformer-based intrusion detection with surrogate decision trees and LLM-evaluated explanations to deliver 99.9% accuracy and 16 high-fidelity logical rules on 5G IoT traffic while preserving performance.
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Heterogeneous Graph Neural Networks with Post-hoc Explanations for Multi-modal and Explainable Land Use Inference
Heterogeneous graph neural networks with post-hoc explanations improve accuracy on six land-use indicators from mobility data and provide feature attribution and counterfactual insights aligned with commuting patterns.