Continual intrusion detection must absorb newly emerging attack stages while retaining legacy detection capability under strict operational constraints, including bounded compute and qubit budgets and privacy rules that preclude long-term storage of raw telemetry. We propose QCL-IDS, a quantum-centric continual-learning framework that co-designs stability and privacy-governed rehearsal for NISQ-era pipelines. Its core component, Q-FISH (Quantum Fisher Anchors), enforces retention using a compact anchor coreset through (i) sensitivity-weighted parameter constraints and (ii) a fidelity-based functional anchoring term that directly limits decision drift on representative historical traffic. To regain plasticity without retaining sensitive flows, QCL-IDS further introduces privacy-preserved quantum generative replay (QGR) via frozen, task-conditioned generator snapshots that synthesize bounded rehearsal samples. Across a three-stage attack stream on UNSW-NB15 and CICIDS2017, QCL-IDS consistently attains the best retention-adaptation trade-off: the gradient-anchor configuration achieves mean Attack-F1 = 0.941 with forgetting = 0.005 on UNSW-NB15 and mean Attack-F1 = 0.944 with forgetting = 0.004 on CICIDS2017, versus 0.800/0.138 and 0.803/0.128 for sequential fine-tuning, respectively.

翻译：持续入侵检测必须在严格的操作约束下吸收新出现的攻击阶段，同时保留对历史攻击的检测能力。这些约束包括有限的计算与量子比特资源，以及禁止长期存储原始遥测数据的隐私规则。我们提出了QCL-IDS，一个以量子为中心的持续学习框架，为NISQ时代的处理流程协同设计了稳定性与隐私约束下的回放机制。其核心组件Q-FISH（量子费希尔锚）通过一个紧凑的锚定核心集来强化知识保留，具体包括：（i）基于敏感度的参数约束，以及（ii）一个基于保真度的功能锚定项，该项直接限制模型在代表性历史流量上的决策漂移。为了在不保留敏感流量的情况下恢复模型可塑性，QCL-IDS进一步引入了隐私保护的量子生成式回放（QGR），该方法利用冻结的、任务条件化的生成器快照来合成有限数量的回放样本。在UNSW-NB15和CICIDS2017数据集上的三阶段攻击流实验中，QCL-IDS始终取得了最佳的保留-适应权衡：梯度锚定配置在UNSW-NB15上实现了平均攻击F1分数=0.941且遗忘度=0.005，在CICIDS2017上实现了平均攻击F1分数=0.944且遗忘度=0.004；相比之下，顺序微调的结果分别为0.800/0.138和0.803/0.128。