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A Framework for Malicious Traffic Detection in IoT Healthcare Environment

The Internet of things (IoT) has emerged as a topic of intense interest among the research and industrial community as it has had a revolutionary impact on human life. The rapid growth of IoT technology has revolutionized human life by inaugurating the concept of smart devices, smart healthcare, sma...

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Autores principales: Hussain, Faisal, Abbas, Syed Ghazanfar, Shah, Ghalib A., Pires, Ivan Miguel, Fayyaz, Ubaid U., Shahzad, Farrukh, Garcia, Nuno M., Zdravevski, Eftim
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8123414/
https://www.ncbi.nlm.nih.gov/pubmed/33925813
http://dx.doi.org/10.3390/s21093025
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author Hussain, Faisal
Abbas, Syed Ghazanfar
Shah, Ghalib A.
Pires, Ivan Miguel
Fayyaz, Ubaid U.
Shahzad, Farrukh
Garcia, Nuno M.
Zdravevski, Eftim
author_facet Hussain, Faisal
Abbas, Syed Ghazanfar
Shah, Ghalib A.
Pires, Ivan Miguel
Fayyaz, Ubaid U.
Shahzad, Farrukh
Garcia, Nuno M.
Zdravevski, Eftim
author_sort Hussain, Faisal
collection PubMed
description The Internet of things (IoT) has emerged as a topic of intense interest among the research and industrial community as it has had a revolutionary impact on human life. The rapid growth of IoT technology has revolutionized human life by inaugurating the concept of smart devices, smart healthcare, smart industry, smart city, smart grid, among others. IoT devices’ security has become a serious concern nowadays, especially for the healthcare domain, where recent attacks exposed damaging IoT security vulnerabilities. Traditional network security solutions are well established. However, due to the resource constraint property of IoT devices and the distinct behavior of IoT protocols, the existing security mechanisms cannot be deployed directly for securing the IoT devices and network from the cyber-attacks. To enhance the level of security for IoT, researchers need IoT-specific tools, methods, and datasets. To address the mentioned problem, we provide a framework for developing IoT context-aware security solutions to detect malicious traffic in IoT use cases. The proposed framework consists of a newly created, open-source IoT data generator tool named IoT-Flock. The IoT-Flock tool allows researchers to develop an IoT use-case comprised of both normal and malicious IoT devices and generate traffic. Additionally, the proposed framework provides an open-source utility for converting the captured traffic generated by IoT-Flock into an IoT dataset. Using the proposed framework in this research, we first generated an IoT healthcare dataset which comprises both normal and IoT attack traffic. Afterwards, we applied different machine learning techniques to the generated dataset to detect the cyber-attacks and protect the healthcare system from cyber-attacks. The proposed framework will help in developing the context-aware IoT security solutions, especially for a sensitive use case like IoT healthcare environment.
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spelling pubmed-81234142021-05-16 A Framework for Malicious Traffic Detection in IoT Healthcare Environment Hussain, Faisal Abbas, Syed Ghazanfar Shah, Ghalib A. Pires, Ivan Miguel Fayyaz, Ubaid U. Shahzad, Farrukh Garcia, Nuno M. Zdravevski, Eftim Sensors (Basel) Article The Internet of things (IoT) has emerged as a topic of intense interest among the research and industrial community as it has had a revolutionary impact on human life. The rapid growth of IoT technology has revolutionized human life by inaugurating the concept of smart devices, smart healthcare, smart industry, smart city, smart grid, among others. IoT devices’ security has become a serious concern nowadays, especially for the healthcare domain, where recent attacks exposed damaging IoT security vulnerabilities. Traditional network security solutions are well established. However, due to the resource constraint property of IoT devices and the distinct behavior of IoT protocols, the existing security mechanisms cannot be deployed directly for securing the IoT devices and network from the cyber-attacks. To enhance the level of security for IoT, researchers need IoT-specific tools, methods, and datasets. To address the mentioned problem, we provide a framework for developing IoT context-aware security solutions to detect malicious traffic in IoT use cases. The proposed framework consists of a newly created, open-source IoT data generator tool named IoT-Flock. The IoT-Flock tool allows researchers to develop an IoT use-case comprised of both normal and malicious IoT devices and generate traffic. Additionally, the proposed framework provides an open-source utility for converting the captured traffic generated by IoT-Flock into an IoT dataset. Using the proposed framework in this research, we first generated an IoT healthcare dataset which comprises both normal and IoT attack traffic. Afterwards, we applied different machine learning techniques to the generated dataset to detect the cyber-attacks and protect the healthcare system from cyber-attacks. The proposed framework will help in developing the context-aware IoT security solutions, especially for a sensitive use case like IoT healthcare environment. MDPI 2021-04-26 /pmc/articles/PMC8123414/ /pubmed/33925813 http://dx.doi.org/10.3390/s21093025 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Hussain, Faisal
Abbas, Syed Ghazanfar
Shah, Ghalib A.
Pires, Ivan Miguel
Fayyaz, Ubaid U.
Shahzad, Farrukh
Garcia, Nuno M.
Zdravevski, Eftim
A Framework for Malicious Traffic Detection in IoT Healthcare Environment
title A Framework for Malicious Traffic Detection in IoT Healthcare Environment
title_full A Framework for Malicious Traffic Detection in IoT Healthcare Environment
title_fullStr A Framework for Malicious Traffic Detection in IoT Healthcare Environment
title_full_unstemmed A Framework for Malicious Traffic Detection in IoT Healthcare Environment
title_short A Framework for Malicious Traffic Detection in IoT Healthcare Environment
title_sort framework for malicious traffic detection in iot healthcare environment
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8123414/
https://www.ncbi.nlm.nih.gov/pubmed/33925813
http://dx.doi.org/10.3390/s21093025
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