SDN orchestration to combat evolving cyber threats in Internet of Medical Things (IoMT)

Shahzana Liaqat, Adnan Akhunzada, Fatema Sabeen Shaikh, Athanasios Giannetsos, Mian Ahmad Jan*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

Internet of Medical Things (IoMT) is now worth a billion dollar market. While offering enormous benefit, the prevalent and open environment of IoMT ecosystem can be a potential target of varied evolving cyber threats and attacks. Further, extensive connectivity of IoMT devices and their dynamic massive heterogeneous communication can create a new attack surface for sophisticated multivector malware attacks. There is a dire need to protect the forthcoming IoMT industrial revolution from varied evolving cyber threats and attacks. The authors propose a hybrid DL-driven SDN-enabled IoMT framework leveraging Convolutional Neural Network (CNN) and Cuda Deep Neural Network Long Short Term Memory (cuDNNLSTM) for a timely and efficient detection of sophisticated multivector malware botnets. For comprehensive evaluation, a state-of-the-art IoMT dataset and standard performance metrics have been employed. For verification purpose, we compare our proposed framework with our constructed hybrid DL-driven architectures and benchmark algorithms. Our proposed technique outperforms in terms of detection accuracy and testing efficiency. Finally, we also perform 10-fold cross validation to utterly show unbiased results.

Original languageEnglish
JournalComputer Communications
Volume160
Pages (from-to)697-705
ISSN0140-3664
DOIs
Publication statusPublished - 1 Jul 2020

Keywords

  • Botnet detection
  • Deep Learning (DL)
  • Hybrid deep learning models
  • Industrial Internet of Things (IIoT)
  • Internet of Medical Things (IoMT)
  • Software Defined Networking (SDN)

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