Federated Machine Learning to Enable Intrusion Detection Systems in IoT Networks

Intrusion detection systems for internet-of-things devices are becoming more relevant as the international reliance on internet-of-things devices grows. Federated learning is one of the most promising areas of study in AI-driven intrusion detection systems in the internet of things and networking, being able to mitigate some of the more severe hardware requirements. Using a federated learning framework, we trained and evaluated several machine learning models to identify distributed denial-of-service attacks in IoT systems. Our framework introduces a novel approach to data preparation for federated learning, incorporating new processing techniques to maximise performance on real-world non-synthetic data. The results show that our proposed first-of-its-kind federated SVM model is highly effective for intrusion detection and matches or outperforms the benchmark algorithms in terms of the attack prediction accuracy, while demonstrating its feasibility for deployment on edge devices. We also compare the physical metrics to conduct one of the first comprehensive evaluations of model suitability for resource-constrained IoT networks, providing valuable insights into the trade-offs between detection accuracy and computational efficiency.