A Secure Ensemble Learning-Based Fog-Cloud Approach for Cyberattack Detection in IoMT

Fazlullah Khan, Mian Ahmad Jan, Ryan Alturki, Mohammad Dahman Alshehri, Syed Tauhidullah Shah, Ateeq Ur Rehman

Research output: Journal PublicationArticlepeer-review

15 Citations (Scopus)

Abstract

The Internet of Medical Things (IoMT) effectively tackles several shortcomings of conventional healthcare systems. It includes medical personnel shortages, patient care quality, insufficient medical supplies, and healthcare expenditures. There are several advantages of using IoMT technology for enhanced treatment efficiency and quality, thus improving patient health. However, the frequency and magnitude of cyberattacks on IoMT are increasing at a breakneck pace. Therefore, this article proposes a cyberattack detection method for IoMT-based networks using ensemble learning and fog-cloud architecture to address security issues. The ensemble technique employs a set of long short-term memory (LSTM) networks as individual learners at the first level and stacks a decision tree on top of them to classify attack and normal events. In addition, we present a framework for deploying the proposed IoMT-based approach as Infrastructure as a Service in the cloud and Software as a Service in the fog. The proposed method is evaluated on the telemetry datasets of IoT and IIoT sensors (ToN-IoT) dataset, and the outcomes reveal that it surpasses the baseline approaches in terms of precision by 4%.

Original languageEnglish
Pages (from-to)10125-10132
Number of pages8
JournalIEEE Transactions on Industrial Informatics
Volume19
Issue number10
DOIs
Publication statusPublished - 1 Oct 2023
Externally publishedYes

Keywords

  • Cloud computing
  • Internet of Medical Things (IoMT)
  • LSTM
  • cybersecurity
  • fog computing
  • healthcare
  • intrusion detection systems

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Electrical and Electronic Engineering

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