The proliferation of chronic disorders such as COVID-19 has recognized the importance of people all over the world having immediate access to healthcare. The recent pandemic has shown deficiencies in the traditional healthcare infrastructure, namely that hospitals and clinics alone are inadequate for grappling with such a disaster. One of the key technologies that favours new healthcare solutions is smart and interconnected wearables. Thanks to developments in the Internet of Things (IoT), these wearables will now collect data on an unprecedented scale. However, as a result of their extensive use, security in these critical systems has become a major concern. This paper presents an intrusion detection mechanism based on Machine Learning Algorithms for healthcare applications used in home network environments. Experiments are carried out on a home network to detect attacks against a health care application. Experiments using the proposed mechanism based on Machine Learning algorithms to detect attacks against a healthcare application are carried out on a home network, and the results show a good performance of the used algorithms.
Pallavi Arora1,Baljeet Kaur2, Marcio Andrey Teixeira3
I.K. Gujral Punjab Technical University, India1,Guru Nanak Dev Engineering College, India2, Federal Institute of Education, Science, and Technology of Sao Paulo, Brazil3
IoMT, Security, Smart Watch, IDS | Published By :
ICTACT
Published In :
ICTACT Journal on Communication Technology
( Volume: 12 , Issue: 4 )
Date of Publication :
December 2021
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