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.

We adopt Industry 4.0 (I4.0) and professional qualifications for adapting models of deliveries of teaching the module of Cyber Security at the University of Lincoln (UK). To achieve this, we investigate I4.0, the challenges it sets to higher education, and professional qualifications. Our findings are used to devise three models of delivery, namely Comprehensive, Partial and Merged. Our discussions show that our strategy of the integration of I4.0 within the curriculum development effectively prepares students to stand out from the crowd by possessing industry ready accreditations along their computer science degrees and the skills required for their future career in cyber security.