Evaluating the Robustness of LSTM-Based Intrusion Detection Against Quantum Circuit Learning Attacks in BB84 Quantum Key Distribution

This paper evaluates the robustness of an LSTM-based intrusion detection system (IDS) designed for BB84 quantum key distribution (QKD) against trainable quantum circuit learning (QCL) attacks and classical fast gradient sign method (FGSM) perturbations. Using the repository-provided experimental outputs as the only source of truth, we first establish the baseline defender performance with 88.5% accuracy and 0.990 AUROC. We then show that both FGSM and QCL attacks reduce detection performance, with FGSM (ε=0.2) causing a 28.6 pp accuracy drop and the selected QCL attacker (6Q-2L, ε=0.2) causing a 9.1 pp accuracy drop. However, the QCL attacker produces a more severe downstream impact on approximate secure key rate: QCL reduces the mean SKR by 40.2% (from 0.082 to 0.049), compared with a 7.9% reduction under FGSM (from 0.082 to 0.076), reflecting a complementary threat profile in the security metric that matters most. The QCL architecture ablation identifies a feasible E3 (6Q-2L) configuration with 6 qubits and 2 layers as the downstream attacker used in later experiments. Adversarial fine-tuning substantially recovers clean and attacked performance, reducing the robustness gap while preserving acceptable clean accuracy. Finally, an approximate, feature-level BB84 secret key rate (SKR) analysis indicates that attack-induced QBER increases correspond to lower estimated SKR, and that fine-tuning partially mitigates this degradation. The study therefore demonstrates a practical robustness trade-off: trainable QCL attacks can materially weaken an IDS, but targeted adversarial fine-tuning restores much of the lost robustness without claiming any quantum advantage or a full cryptographic break.