Ensuring Security Continuum from Edge to Cloud : Adaptive Security for IoT-based Critical Infrastructures using FL at the Edge

Securing IoT-based Critical Infrastructures (CI) necessitates a cross-domain management of IT/OT systems encompassing hardware, software, data, and models along with their application scenarios. There is no feasible way to manually secure such heterogeneous, weakly-protected and physically distributed systems. In this work, we propose an adaptive security framework that uses Federated Learning (FL) for IoT data monitoring and analysis at the edge. We use Deep Neural Network (DNN) model selection and switching inside FL to address performance and security problems. We began prototyping the Edge-FL system using single board computers (a Raspberry Pi-5 cluster). We measure and compare the impacts of hardware heterogeneity. The framework aims to provide continuum of security and intelligence from IoT device to Edge to Cloud.