Deciding When Not to Decide: Indeterminacy-Aware Intrusion Detection with NeutroSENSE

TL;DR

NeutroSENSE is an ensemble intrusion detection framework for IoT that leverages neutrosophic logic to quantify uncertainty, enabling abstention and improving trustworthiness in edge security applications.

Contribution

This paper introduces NeutroSENSE, a novel neutrosophic-enhanced ensemble method that incorporates uncertainty quantification for interpretable IoT intrusion detection.

Findings

Achieved 97% accuracy on IoT-CAD dataset

Higher indeterminacy correlates with misclassification

Enables targeted review through uncertainty thresholds

Abstract

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.