Adaptive Intrusion Detection in Heterogeneous IoT Environments Using Federated Graph Neural Networks

In the fast-growing Internet of Things (IoT) environment, intrusion detection is an essential security problem due to the heterogeneity and decentralized characteristic of connected devices. This research introduces a new intrusion detection system (IDS) based on the synergetic combination of Federated Learning (FL) and Graph Neural Networks (GNNs) to solve privacy, accuracy, and resource limitations in IoT environments. The envisioned system allows every IoT device to locally train light-weight GNN models on graph-structured representations of its local network traffic. The local models are then collectively aggregated using FL methods without exposing raw data, thus maintaining privacy. Sophisticated graph construction and temporal encoding techniques are utilized to capture changing attack patterns, and continual learning mechanisms are employed to provide adaptability to novel threats. Performance is measured in terms of detection accuracy, latency, and energy usage. Compared with traditional signature-based (Snort), anomaly-based (K-Means), and deep learning-based (CNN) IDS schemes, the developed GNN+FL mechanism exhibits higher detection accuracy (96%), low latency (75 ms), and energy efficiency (1 W) and is extremely well-suited for low-power, real-time applications. Apart from enhancing real-time detection, this framework also supports scalable and privacy-aware security in distributed IoT