Foundation-Model-Based Federated Learning for Intrusion Detection in Drone-Aided Industrial IoT

Drone networks are becoming increasingly significant in industrial Internet of Things (IIoT) applications. The limited resources of drones pose challenges in implementing robust security mechanisms that require substantial computation and power resources. Specifically, the inherent complexity of drone networks makes traditional intrusion detection systems (IDS) ineffective due to data imbalance and data scarcity. To address these challenges, this paper proposes a novel IDS framework that integrates conditional generative adversarial networks (CGANs) and utilizes the benefits from the systematic integration of foundation models within a federated learning (FL) paradigm. It leverages the CGANs to address the data issues ensures reliable performance and stable convergence against the foundation model. Moreover, our approach enhances data privacy relying on the differential privacy in FL and protects global model integrity through secure aggregation and updating. Simulation results show that the proposed framework achieve the accuracy rates of 91% and 99% on cyber and physical datasets, respectively. This framework achieves improvement ranging from 0.47% to 3.24% for cyber datasets and from 0.93% to 4.84% for physical datasets, which yields its superior performance in drone networks intrusion detection.