  Researchers are exploring the integration of IoT and the cloud continuum,
together with AI to enhance the cost-effectiveness and efficiency of critical
infrastructure (CI) systems. This integration, however, increases
susceptibility of CI systems to cyberattacks, potentially leading to
disruptions like power outages, oil spills, or even a nuclear mishap. CI
systems are inherently complex and generate vast amounts of heterogeneous and
high-dimensional data, which crosses many trust boundaries in their journey
across the IoT, edge, and cloud domains over the communication network
interconnecting them. As a result, they face expanded attack surfaces. To
ensure the security of these dataflows, researchers have used deep neural
network models with encouraging results. Nevertheless, two important challenges
that remain are tackling the computational complexity of these models to reduce
convergence times and preserving the accuracy of detection of
integrity-violating intrusions. In this paper, we propose an innovative
approach that utilizes trained edge cloud models to synthesize central cloud
models, effectively overcoming these challenges. We empirically validate the
effectiveness of the proposed method by comparing it with traditional
centralized and distributed techniques, including a contemporary collaborative
technique.
