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Article

A Framework for Malicious Traffic Detection in IoT Healthcare Environment

1
Al-Khwarizmi Institute of Computer Science (KICS), University of Engineering & Technology (UET), Lahore 54890, Pakistan
2
Instituto de Telecomunicações, Universidade da Beira Interior, 6200-001 Covilhã, Portugal
3
Computer Science Department, Polytechnic Institute of Viseu, 3504-510 Viseu, Portugal
4
UICISA: E Research Centre, School of Health, Polytechnic Institute of Viseu, 3504-510 Viseu, Portugal
5
Faculty of Computer Science and Engineering, University Ss Cyril and Methodius, 1000 Skopje, North Macedonia
*
Authors to whom correspondence should be addressed.
Academic Editors: Hans Hallez, Bart Vanrumste, Wei Chen and Roozbeh Jafari
Sensors 2021, 21(9), 3025; https://doi.org/10.3390/s21093025
Received: 1 April 2021 / Revised: 17 April 2021 / Accepted: 22 April 2021 / Published: 26 April 2021
(This article belongs to the Special Issue Wearables and IoT Sensors for Applications in Healthcare)
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. View Full-Text
Keywords: Internet of Things (IoT); IoT healthcare systems; healthcare monitoring; machine learning; securing healthcare systems; IoT healthcare dataset; IoT traffic generator; IoT flock; healthcare security; intrusion detection Internet of Things (IoT); IoT healthcare systems; healthcare monitoring; machine learning; securing healthcare systems; IoT healthcare dataset; IoT traffic generator; IoT flock; healthcare security; intrusion detection
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MDPI and ACS Style

Hussain, F.; Abbas, S.G.; Shah, G.A.; Pires, I.M.; Fayyaz, U.U.; Shahzad, F.; Garcia, N.M.; Zdravevski, E. A Framework for Malicious Traffic Detection in IoT Healthcare Environment. Sensors 2021, 21, 3025. https://doi.org/10.3390/s21093025

AMA Style

Hussain F, Abbas SG, Shah GA, Pires IM, Fayyaz UU, Shahzad F, Garcia NM, Zdravevski E. A Framework for Malicious Traffic Detection in IoT Healthcare Environment. Sensors. 2021; 21(9):3025. https://doi.org/10.3390/s21093025

Chicago/Turabian Style

Hussain, Faisal, Syed G. Abbas, Ghalib A. Shah, Ivan M. Pires, Ubaid U. Fayyaz, Farrukh Shahzad, Nuno M. Garcia, and Eftim Zdravevski. 2021. "A Framework for Malicious Traffic Detection in IoT Healthcare Environment" Sensors 21, no. 9: 3025. https://doi.org/10.3390/s21093025

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