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.

翻译：研究人员正在探索将物联网与云连续体相结合，并引入人工智能技术，以提升关键基础设施系统的成本效益与运行效率。然而，这种融合也增加了关键基础设施系统遭受网络攻击的脆弱性，可能导致电力中断、石油泄漏甚至核事故等严重破坏。关键基础设施系统本身具有高度复杂性，会产生海量异构高维数据，这些数据在通过互联通信网络跨越物联网、边缘计算和云域传输时，需穿越多个信任边界，从而导致攻击面显著扩大。为确保数据流安全，研究人员已采用深度神经网络模型并取得显著成效。然而，当前仍面临两大挑战：一是应对模型计算复杂度以缩短收敛时间，二是保持对完整性破坏型入侵检测的准确性。本文提出一种创新方法，利用经过训练的边缘云模型合成中心云模型，从而有效应对上述挑战。我们通过与传统集中式、分布式技术及前沿协作技术的对比实验，实证验证了所提方法的有效性。