ConformalShift 攻击心电监测，不改波形和标签，只重排真实事件顺序，成功率从 4.4% 提到 66.7%。
ConformalShift 是一种针对自适应心电图监测的有界事件重排攻击。在 MIT-BIH 确认记录上，它不改动波形、标签、分类器分数或事件多重集，只重排真实先前事件来降低目标的心室阈值。攻击对 Extra Trees 和 HistGradientBoosting 的合格目标抑制率分别达到 66.7% 和 60.0%，随机调度仅为 4.4% 和 12.0%。迁移到 INCART 后仍优于随机调度，收缩位移预算削弱了攻击效果。
ConformalShift: Targeted Event Reordering Against Adaptive ECG Monitoring
Adaptive conformal prediction can recover clinically important heartbeat classes missed by a point classifier, but delayed feedback makes its decisions sensitive to event order. We introduce ConformalShift, a bounded event-reordering attack that suppresses the ventricular class for rescued events without modifying ECG waveforms, labels, classifier scores, or the event multiset. ConformalShift searches for feasible permutations of authentic preceding events that lower the ventricular threshold before a selected target is evaluated. On disjoint MIT--BIH confirmation records, the attack suppressed 66.7% of eligible targets for Extra Trees and 60.0% for HistGradientBoosting, compared with random-schedule rates of 4.4% and 12.0%, respectively. Transferred configurations also outperformed random scheduling on INCART, while reducing the displacement budget weakened the attack on both datasets. These results show that adaptive monitors in healthcare can be compromised through the timing of authentic information, even when waveforms, labels, classifier outputs, and event contents remain unchanged.