QCL-IDS: Quantum Continual Learning for Intrusion Detection with Fidelity-Anchored Stability and Generative Replay

• URL: http://arxiv.org/abs/2601.21318v1
• Date: Thu, 29 Jan 2026 06:27:05 GMT
• Title: QCL-IDS: Quantum Continual Learning for Intrusion Detection with Fidelity-Anchored Stability and Generative Replay
• Abstract summary: Continual intrusion detection must absorb newly emerging attack stages while retaining legacy detection capability under strict operational constraints.<n>We propose QCL-IDS, a quantum-centric continual-learning framework that co-designs stability and privacy-governed rehearsal for NISQ-era pipelines.
• Score: 7.4107299737728445
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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