Proactive Defense for Fog-to-Things Critical Infrastructure

Robust and adaptable cybersecurity mechanisms are needed to mitigate sophisticated and future zero-day cyberattacks and threats, particularly in the dynamic Fog-to-Things (FoT) computational paradigm which makes use of massively distributed nodes. Deep learning (DL)-driven architectures have been proven more successful in big data areas than classical Machine Learning (ML) based algorithms. We orchestrate the Software Defined Network (SDN) control plane to propose a highly scalable proactive defense mechanism leveraging Cuda-Deep Neural Network Gated Recurrent Unit (CU-DNNGRU) for the FoT critical computing infrastructure. Besides, the proposed framework does not place an extra burden on the underlying energy and power-constrained FoT devices.We used the current state-of-the-art dataset (i.e., CICIDS2018) and evaluated our approach using standard performance metrics. We compared our proposed technique with our constructed hybrid DL-driven architectures and benchmark DL algorithms to evaluate its performance and efficacy. We hope that this work will enable further security research in the next generation FoT computational paradigms.