通过调整心跳事件顺序，可绕过自适应心电监测的异常检测
ConformalShift: Targeted Event Reordering Against Adaptive ECG Monitoring
- 在不修改波形和标签的前提下，重排真实事件顺序以降低心室分类阈值
- 对额外树和直方图提升梯度模型，成功抑制率达66.7%与60.0%
- 揭示医疗自适应系统易受事件时间顺序攻击，适用于安全评估研究者
自适应共形预测能恢复点分类器遗漏的重要心跳类别，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，可在不修改心电波形、标签、分类器得分或事件集合的情况下，抑制被挽救事件的心室类。该方法搜索真实前置事件的可行排列，使目标事件评估前心室阈值降低。在独立的MIT-BIH确认记录上，该攻击对额外树和直方图提升梯度模型分别抑制了66.7%和60.0%的合格目标，远高于随机调度的4.4%和12.0%。迁移配置在INCART数据集上也优于随机调度，而减小扰动预算会削弱攻击效果。结果表明，即使波形、标签、分类输出和事件内容不变，医疗自适应监测系统仍可能因真实信息的时间顺序被攻破。
原文摘要 · Abstract (English)
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.
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