Proactive Defense for Fog-to-Things Critical Infrastructure

Muhammad Taimoor Khan, Adnan Akhunzada, Sherali Zeadally

Research output: Contribution to journalArticlepeer-review

19 Scopus citations

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 languageEnglish
Pages (from-to)44-49
Number of pages6
JournalIEEE Communications Magazine
Volume60
Issue number12
DOIs
StatePublished - 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

Fingerprint

Dive into the research topics of 'Proactive Defense for Fog-to-Things Critical Infrastructure'. Together they form a unique fingerprint.

Cite this