A framework for malicious traffic detection in IoT healthcare environment
Journal
Sensors
Date Issued
2021-04-26
Author(s)
Hussain, Faisal
Abbas, Syed Ghazanfar
A Shah, Ghalib
Miguel Pires, Ivan
U Fayyaz, Ubaid
Shahzad, Farrukh
M Garcia, Nuno
Abstract
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 IoTspecific 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 IoTFlock. 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.
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 IoTspecific 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 IoTFlock. 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.
Subjects
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