Intrusion Detection Systems for the Internet of Drones Security Using Multi-Teacher Knowledge Distillation
Mostafa Ogab, Sofiane Zaidi, Abdelhabib Bourouis, Carlos T. Calafate · IEEE Access · 2025
The Internet of Drones (IoD) is increasingly adopted in mission-critical applications, but it remains vulnerable to a wide range of cyberattacks due to its open communication channels and resource-constrained nodes. Existing Intrusion Detection Systems (IDS) often neglect class imbalance and computational limitations, resulting in poor generalization and impractical deployment. This study aims to develop an efficient and deployable IDS framework for the IoD by addressing two key challenges: (i) the severe class imbalance in real-world traffic datasets, and (ii) the high computational complexity of Deep Learning (DL)-based models. We propose a two-phase methodology. In Phase 1, validated undersampling strategies are applied to rebalance the CIC-IoT2023 dataset, after which baseline classifiers are trained to assess detection performance. In Phase 2, the best-performing models serve as teacher networks in a multi-teacher knowledge distillation (MTKD) framework—implemented with average-weighted and confidence-weighted variants—to train lightweight Multi-Layer Perceptron (MLP) student models of varying capacities. These students are optimized for deployment on resource-constrained IoD edge devices. Effectiveness is assessed using both classification and computational efficiency metrics. Results show that Instance Hardness Threshold undersampling consistently outperformed Random Under Sampler across all baseline models, enhancing detection of minority attack classes. The best-performing models—LSTM and XGBoost—were selected as teachers for MTKD. Applying average-weighted and confidence-weighted MTKD to MLP1x8 increased accuracy by 0.03% and 0.09%, respectively, while confidence-weighted also reduced inference time by 0.0006 s. These gains were achieved without increasing the computational footprint, as the model retained its minimal size (0.0039 MB) and low parameter count (448). The proposed two-phase framework provides a practical solution for building lightweight and accurate IDS models suitable for real-time deployment in resource-constrained drone environments.