Safety monitoring is essential for Cyber-Physical Systems (CPSs). However, unsafe events are rare in real-world CPS operations, creating an extreme class imbalance that degrades safety predictors. Standard rebalancing techniques perform poorly on time-series CPS telemetry, either generating unrealistic synthetic samples or overfitting on the minority class. Meanwhile, behavioral uncertainty in CPS operations, defined as the degree of doubt or uncertainty in CPS decisions , is often correlated with safety outcomes but unexplored in safety monitoring. To that end, we propose U-Balance, a supervised approach that leverages behavioral uncertainty to rebalance imbalanced datasets prior to training a safety predictor. U-Balance first trains a GatedMLP-based uncertainty predictor that summarizes each telemetry window into distributional kinematic features and outputs an uncertainty score. It then applies an uncertainty-guided label rebalancing (uLNR) mechanism that probabilistically relabels \textit{safe}-labeled windows with unusually high uncertainty as \textit{unsafe}, thereby enriching the minority class with informative boundary samples without synthesizing new data. Finally, a safety predictor is trained on the rebalanced dataset for safety monitoring. We evaluate U-Balance on a large-scale UAV benchmark with a 46:1 safe-to-unsafe ratio. Results confirm a moderate but significant correlation between behavioral uncertainty and safety. We then identify uLNR as the most effective strategy to exploit uncertainty information, compared to direct early and late fusion. U-Balance achieves a 0.806 F1 score, outperforming the strongest baseline by 14.3 percentage points, while maintaining competitive inference efficiency. Ablation studies confirm that both the GatedMLP-based uncertainty predictor and the uLNR mechanism contribute significantly to U-Balance's effectiveness.

翻译：安全监控对信息物理系统（CPS）至关重要。然而，真实CPS运行中危险事件极为罕见，导致极端类别不平衡问题，降低了安全预测器的性能。标准重平衡技术在时间序列CPS遥测数据上表现不佳，要么生成不现实的合成样本，要么在少数类上过拟合。与此同时，CPS运行中的行为不确定性（定义为CPS决策的怀疑或不确定程度）通常与安全结果相关，但在安全监控中尚未被探索。为此，我们提出U-Balance，一种监督方法，在训练安全预测器之前利用行为不确定性对不平衡数据集进行重平衡。U-Balance首先训练基于GatedMLP的不确定性预测器，将每个遥测窗口总结为分布运动学特征并输出不确定性评分。接着应用不确定性引导的标签重平衡（uLNR）机制，以概率方式将具有异常高不确定性的"安全"标签窗口重新标记为"危险"，从而在不生成新数据的情况下丰富少数类中的信息边界样本。最终，在重平衡数据集上训练安全预测器用于安全监控。我们在安全-危险比为46:1的大规模无人机基准数据集上评估U-Balance。结果证实行为不确定性与安全性之间存在中等但显著的相关性。进一步发现，与直接早期融合和晚期融合相比，uLNR是利用不确定性信息最有效的策略。U-Balance取得0.806的F1分数，超越最强基线14.3个百分点，同时保持具有竞争力的推理效率。消融研究证实，基于GatedMLP的不确定性预测器和uLNR机制均对U-Balance的有效性做出重要贡献。