Lightweight Multi-Attribute Physical Layer Authentication in LoRaWAN: A Comparative Study of Knowledge Distillation and Direct Training

LoRaWAN has become a widely adopted communication technology for Internet of Things applications because of its long-range communication capability and low power consumption. However, resource-constrained LoRaWAN networks remain vulnerable to physical-layer attacks such as jamming and battery-depletion attacks. This paper investigates architectural compression and Knowledge Distillation (KD) for lightweight multi-attribute Physical Layer Authentication (PLA) in LoRaWAN networks. A higher-capacity Teacher model is used to train progressively smaller Student models, and three Student-training strategies—Direct training, Hard-KD, and Soft-KD—are systematically compared to determine whether Teacher supervision provides an advantage beyond architectural compression alone. The framework uses the Received Signal Strength Indicator (RSSI), altitude, and the battery level as input attributes and is evaluated offline using a device-aware semi-synthetic dataset derived from the publicly available Brno LoRaWAN dataset. The resulting dataset contains Normal Communication, Jamming Attack, and Battery Depletion Attack traffic. The experimental results show that Student (8) preserves a classification performance close to that of the Teacher across all three training strategies, achieving Macro F1-scores of 90.619 ± 0.056% for Direct training, 90.591 ± 0.069% for Hard-KD, and 90.626 ± 0.083% for Soft-KD, compared with 91.007% for the Teacher. Student (8) reduces the number of trainable parameters from 2435 to 59 (97.6%) and the serialised model size from 46.65 KB to 2.80 KB (94.0%), while exhibiting a substantially higher measured batch-processing throughput under the evaluated CPU-based implementation. A feature-ablation analysis further indicates that the RSSI and battery level provide the dominant discriminatory information, while the altitude provides a smaller but measurable contextual contribution. The comparable performance of Direct, Hard-KD, and Soft-KD demonstrates that KD does not consistently outperform direct training for the evaluated Student architectures; rather, architectural compression itself accounts for much of the retained performance. These findings demonstrate that compact Student architectures can substantially reduce the model complexity while preserving a strong classification performance within the evaluated multi-attribute PLA framework, supporting the further investigation of lightweight network-side security mechanisms for LoRaWAN deployments.