Proactive Defense for Fog-to-Things Critical Infrastructure

Muhammad Taimoor Khan, Adnan Akhunzada, Sherali Zeadally

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


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.

Original languageEnglish
Pages (from-to)1-7
Number of pages7
JournalIEEE Communications Magazine
StateAccepted/In press - 2022

Bibliographical note

Publisher Copyright:


  • Computer architecture
  • Critical infrastructure
  • Edge computing
  • Intrusion detection
  • Performance evaluation
  • Ransomware
  • Security

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Networks and Communications
  • Electrical and Electronic Engineering


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