research-article

Intelligent Control and Security of Fog Resources in Healthcare Systems via a Cognitive Fog Model

Authors:

Mohammed Al-Khafajiy,

Zakaria Maamar,

Moayad Aloqaily,

Martin RandlesAuthors Info & Claims

ACM Transactions on Internet Technology (TOIT), Volume 21, Issue 3

Article No.: 54, Pages 1 - 23

https://doi.org/10.1145/3382770

Published: 16 June 2021 Publication History

Abstract

There have been significant advances in the field of Internet of Things (IoT) recently, which have not always considered security or data security concerns: A high degree of security is required when considering the sharing of medical data over networks. In most IoT-based systems, especially those within smart-homes and smart-cities, there is a bridging point (fog computing) between a sensor network and the Internet which often just performs basic functions such as translating between the protocols used in the Internet and sensor networks, as well as small amounts of data processing. The fog nodes can have useful knowledge and potential for constructive security and control over both the sensor network and the data transmitted over the Internet. Smart healthcare services utilise such networks of IoT systems. It is therefore vital that medical data emanating from IoT systems is highly secure, to prevent fraudulent use, whilst maintaining quality of service providing assured, verified and complete data. In this article, we examine the development of a Cognitive Fog (CF) model, for secure, smart healthcare services, that is able to make decisions such as opting-in and opting-out from running processes and invoking new processes when required, and providing security for the operational processes within the fog system. Overall, the proposed ensemble security model performed better in terms of Accuracy Rate, Detection Rate, and a lower False Positive Rate (standard intrusion detection measurements) than three base classifiers (K-NN, DBSCAN, and DT) using a standard security dataset (NSL-KDD).

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Haleem AJavaid M(2024)Role of cognitive computing in enhancing innovative healthcare solutionsAdvances in Biomarker Sciences and Technology10.1016/j.abst.2024.08.0026(152-165)Online publication date: 2024
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Index Terms

Intelligent Control and Security of Fog Resources in Healthcare Systems via a Cognitive Fog Model
1. Computer systems organization
  1. Dependable and fault-tolerant systems and networks
    1. Redundancy
  2. Embedded and cyber-physical systems
    1. Embedded systems
    2. Robotics
2. Networks
  1. Network properties
    1. Network reliability

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Published In

cover image ACM Transactions on Internet Technology

ACM Transactions on Internet Technology Volume 21, Issue 3

August 2021

522 pages

ISSN:1533-5399

EISSN:1557-6051

DOI:10.1145/3468071

Editor:
Ling Liu
Georgia Institute of Technology, USA

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Copyright © 2021 Association for Computing Machinery.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 June 2021

Accepted: 01 February 2021

Online AM: 07 May 2020

Revised: 01 February 2020

Received: 01 December 2019

Published in TOIT Volume 21, Issue 3

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Cited By

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https://doi.org/10.1109/IPSN61024.2024.00021
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https://doi.org/10.1016/j.abst.2024.08.002
Aluvalu RAravinda KMaheswari VKumar KRao BPrasad K(2024)RETRACTED ARTICLE: Designing a cognitive smart healthcare framework for seizure prediction using multimodal convolutional neural networkCognitive Neurodynamics10.1007/s11571-023-10049-xOnline publication date: 2-Jan-2024
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