Deciding When Not to Decide: Indeterminacy-Aware Intrusion Detection with NeutroSENSE
This work addresses the problem of trustworthy AI decisions for IoT security systems, particularly in edge deployments, by providing interpretable uncertainty quantification, though it is incremental as it builds on existing ensemble methods with neutrosophic logic.
The paper tackled intrusion detection in IoT environments by developing NeutroSENSE, a neutrosophic-enhanced ensemble framework that quantifies uncertainty and enables abstention, achieving 97% accuracy on the IoT-CAD dataset with misclassified samples showing higher indeterminacy (I=0.62 vs. I=0.24 for correct ones).
This paper presents NeutroSENSE, a neutrosophic-enhanced ensemble framework for interpretable intrusion detection in IoT environments. By integrating Random Forest, XGBoost, and Logistic Regression with neutrosophic logic, the system decomposes prediction confidence into truth (T), falsity (F), and indeterminacy (I) components, enabling uncertainty quantification and abstention. Predictions with high indeterminacy are flagged for review using both global and adaptive, class-specific thresholds. Evaluated on the IoT-CAD dataset, NeutroSENSE achieved 97% accuracy, while demonstrating that misclassified samples exhibit significantly higher indeterminacy (I = 0.62) than correct ones (I = 0.24). The use of indeterminacy as a proxy for uncertainty enables informed abstention and targeted review-particularly valuable in edge deployments. Figures and tables validate the correlation between I-scores and error likelihood, supporting more trustworthy, human-in-the-loop AI decisions. This work shows that neutrosophic logic enhances both accuracy and explainability, providing a practical foundation for trust-aware AI in edge and fog-based IoT security systems.