ConformalShift：针对自适应心电监测的定向事件重排序攻击
ConformalShift: Targeted Event Reordering Against Adaptive ECG Monitoring
AI总结：
该研究提出ConformalShift攻击，通过重排序真实前置事件降低心电监测的心室阈值，在MIT-BIH和INCART数据集上成功抑制心室类别，表明医疗自适应监测器可被真实信息时序破坏。
AI中文摘要：
自适应共形预测可恢复点分类器遗漏的临床重要心跳类别，但延迟反馈使其决策对事件顺序敏感。我们提出ConformalShift，一种有界事件重排序攻击，该攻击在不修改心电波形、标签、分类器得分或事件多重集的情况下，抑制被拯救事件中的心室类别。ConformalShift搜索真实前置事件的可行排列，以在评估选定目标前降低心室阈值。在独立的MIT-BIH验证记录上，该攻击对Extra Trees抑制了66.7%的合格目标，对HistGradientBoosting抑制了60.0%，而随机调度的对应比例分别为4.4%和12.0%。迁移后的配置在INCART上也优于随机调度，同时位移预算减少会削弱该攻击在两个数据集上的效果。这些结果表明，即使波形、标签、分类器输出和事件内容保持不变，医疗领域的自适应监测器仍可通过真实信息的时序被破坏。
英文摘要：
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.
