Subject: 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
Year: 2021
Type: Article
Title: A framework for malicious traffic detection in IoT healthcare environment
Author: Hussain, Faisal
Author: Abbas, Syed Ghazanfar
Author: A Shah, Ghalib
Author: Miguel Pires, Ivan
Author: U Fayyaz, Ubaid
Author: Shahzad, Farrukh
Author: M Garcia, Nuno
Author: Zdravevski, Eftim
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.
Publisher: MDPI
Relation: Sensors
Identifier: oai:repository.ukim.mk:20.500.12188/20782
Identifier: http://hdl.handle.net/20.500.12188/20782
