Static Quantum Bit Error Rate (QBER) thresholding is the standard defense mechanism in deployed Quantum Key Distribution (QKD) systems. In noisy free-space optical (FSO) channels, however, natural atmospheric variations can camouflage short, low-intensity eavesdropping bursts, rendering fixed thresholds ineffective. This paper investigates physics-aware temporal feature engineering for machine learning-based anomaly detection in entanglement-based BBM92 QKD telemetry. A 24-dimensional feature sp