Abstract
Robust and adaptable cybersecurity mechanisms are needed to mitigate sophisticated and future zero-day cyberattacks and threats, particularly in the dynamic Fog of 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 networking (SDN) control plane to propose a highly scalable proactive defense mechanism leveraging the Cuda-Deep Neural Network Gated Recurrent Unit (CU-DNNGRU) for the FoT critical computing infrastructure. Furthermore, the proposed framework does not place an extra burden on the underlying energy- A nd 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 compare 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.
Original language | English |
---|---|
Pages (from-to) | 44-49 |
Number of pages | 6 |
Journal | IEEE Communications Magazine |
Volume | 60 |
Issue number | 12 |
DOIs | |
State | Published - Dec 1 2022 |
Bibliographical note
Publisher Copyright:© 1979-2012 IEEE.
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Computer Networks and Communications
- Computer Science Applications