用不确定度判断何时该放弃决策，提升物联网安全检测的可信度。
Deciding When Not to Decide: Indeterminacy-Aware Intrusion Detection with NeutroSENSE
- 将预测置信度拆解为真、假、不确定三部分，实现不确定性量化。
- 误判样本的不确定度达0.62，远高于正确样本的0.24，可精准识别风险。
- 适合边缘部署，支持人工介入，让AI决策更可解释、更可靠。
本文提出NeutroSENSE，一种基于中智逻辑的集成式入侵检测框架，用于提升物联网环境中的可解释性。通过融合随机森林、XGBoost与逻辑回归，并引入中智逻辑，系统将预测置信度分解为真（T）、假（F）和不确定（I）三个分量，实现不确定性量化与主动回避。当不确定度较高时，系统会依据全局与自适应的类别特定阈值标记样本供人工审查。在IoT-CAD数据集上的评估显示，该方法达到97%准确率；且误分类样本的不确定度（I = 0.62）显著高于正确样本（I = 0.24）。实验验证了不确定度得分与错误概率之间的强相关性，支持更具信任感的人机协同决策。结果表明，中智逻辑不仅提升了准确率，还增强了可解释性，为边缘与雾计算场景下的可信人工智能提供了实用基础。
原文摘要 · Abstract (English)
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.
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