Federated Transfer Learning-Based IDS for the Internet of Medical Things (IoMT)
2021 IEEE Globecom Workshops, GC Wkshps 2021
; 2021.
Article
in English
| Scopus | ID: covidwho-1746089
ABSTRACT
The Internet of Medical Things (IoMT) is a set of medical devices and applications that connect to healthcare systems through the Internet. Those devices are equipped with communication technologies that allow them to communicate with each other and the Internet. Reliance on the IoMT is increasing with the increase in epidemics and chronic diseases such as COVID-19 and diabetes;with the increase in the number of IoMT users and the need for electronic data sharing and virtual services, cyberattacks in the healthcare sector for accessing confidential patient data has been increasing in the recent years. The healthcare applications and their infrastructures have special requirements for handling sensitive users' data and the need for high availability. Therefore, securing healthcare applications and data has attracted special attention from both industry and researchers. In this paper, we propose a Federated Transfer Learning-based Intrusion Detection System (IDS) to secure the patient's healthcare-connected devices. The model uses Deep Neural Network (DNN) algorithm for training the network and transferring the knowledge from the connected edge models to build an aggregated global model and customizing it for each one of the connected edge devices without exposing data privacy. CICIDS2017 dataset has been used to evaluate the performance in terms of accuracy, detection rate, and average training time. In addition to preserving data privacy of edge devices and achieving better performance, our comparison indicates that the proposed model can be generalized better and learns incrementally compared to other baseline ML/DL algorithms used in the traditional centralized learning schemes. © 2021 IEEE.
Federated Learning (FL); Internet of Medical Things (IoMT); Intrusion Detection System (IDS); Transfer Learning (TL); Computer crime; Data privacy; Deep neural networks; Health care; Hospital data processing; Network security; Federated learning; Health care application; Healthcare systems; Internet of medical thing; Intrusion detection system; Intrusion Detection Systems; Medical Devices; Performance; Transfer learning; Intrusion detection
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Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
2021 IEEE Globecom Workshops, GC Wkshps 2021
Year:
2021
Document Type:
Article
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