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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...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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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. |
format | Online Article Text |
id | pubmed-8123414 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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