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Home Network Security Incorporating Machine Learning Algorithms In Internet Of Medical Things


Affiliations
1 Department of Computer Science and Engineering, I.K. Gujral Punjab Technical University, India
2 Department of Electronics and Communication Engineering, Guru Nanak Dev Engineering College, India
3 Department of Informatics, Federal Institute of Education, Science, and Technology of São Paulo, Brazil
     

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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.

Keywords

IoMT, Security, Smart Watch, IDS
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  • D. Koutras, G. Stergiopoulos, T. Dasaklis, P. Kotzanikolaou, D. Glynos and C. Douligeris, “Security in IoMT Communications: A Survey”, Sensors, Vol. 20, No. 17, pp. 4828-4848, 2020.

  • F. Alsubaei, A. Abuhussein and S. Shiva, “Security and Privacy in the Internet of Medical Things: Taxonomy and Risk Assessment”, Proceedings of IEEE 42nd International Conference on Local Computer Networks, pp. 112-120, 2017.

  • S. S. Hameed, “A Systematic Review of Security and Privacy Issues in the Internet of Medical Things; the Role of Machine Learning Approaches”, Computer Science, Vol. 7, No. 4, pp. 44-56, 2021.

  • M. Papaioannou “A Survey on Security Threats and Countermeasures in Internet of Medical Things (IoMT)”, Emerging Telecommunications Technologies, Vol. 23, pp. 1-12, 2020.

  • A. Hockey, “Uncovering the Cyber Security Challenges in Healthcare”, Network Security, Vol. 2020, No. 4, pp. 18-19, 2020.

  • G. Thamilarasu, A. Odesile and A. Hoang, “An Intrusion Detection System for Internet of Medical Things”, IEEE Access, Vol. 8, pp. 181560-181576, 2020.

  • G. Hatzivasilis, O. Soultatos, C. Verikoukis, G. Demetriou, C.I. Tsatsoulis and N. Systems, “Review of Security and Privacy for the Internet of Medical Things (IoMT)”, Proceedings of IEEE 42nd International Conference on Distributed Computing in Sensor Systems, pp. 1-8, 2019.

  • J.J. Hathaliya and S. Tanwar, “An Exhaustive Survey on Security and Privacy Issues in Healthcare 4.0,” Computer Communications, Vol. 153, pp. 311-335, 2020.

  • H.K. Bharadwaj, A. Agarwal, V. Chamola, V. Hassija, M. Guizani and B. Sikdar, “A Review on the Role of Machine Learning in Enabling IoT Based Healthcare Applications”, Proceedings of IEEE International Conference on AI and IoT for Smart Health, pp. 32-45, 2021.

  • A.I. Newaz, A.K. Sikder, M.A. Rahman and A.S. Uluagac, “HealthGuard: A Machine Learning-Based Security Framework for Smart Healthcare Systems”, Proceedings of IEEE International Conference on Social Networks Analysis, Management and Security, pp. 389-396, 2019.

  • L. Xiao, X. Wan, X. Lu, Y. Zhang and D. Wu, “IoT Security Techniques Based on Machine Learning: How Do IoT Devices Use AI to Enhance Security?”, IEEE Signal Processing Magazine, Vol. 35, No. 5, pp. 41-49, 2018.

  • E. Adi, A. Anwar, Z. Baig and S. Zeadally, “Machine Learning and Data Analytics for the IoT”, Neural Computing and Applications, Vol. 32, No. 20, pp. 1620516233, 2020.

  • S.M. Tahsien, H. Karimipour and P. Spachos, “Machine Learning based Solutions for Security of Internet of Things (IoT): A Survey”, Journal of Network and Computer Applications, Vol. 161, pp. 1-13, 2020.

  • M. Hasan, M.M. Islam, M.I.I. Zarif and M.M.A. Hashem, “Attack and Anomaly Detection in IoT Sensors in IoT Sites using Machine Learning Approaches”, Internet of Things, Vol. 7, pp. 1-19, 2019.

  • A.A. Hady, A. Ghubaish, T. Salman, D. Unal and R. Jain, “Intrusion Detection System for Healthcare Systems Using Medical and Network Data: A Comparison Study”, IEEE Access, Vol. 8, pp. 106576-106584, 2020.

  • Openargus-Home, Available at https://openargus.org/, Accessed at 2021.

  • Pearson Correlation an Overview, Available at https://www.sciencedirect.com/topics/computerscience/pearson-correlation, Accessed at 2021.


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  • Home Network Security Incorporating Machine Learning Algorithms In Internet Of Medical Things

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Authors

Pallavi Arora
Department of Computer Science and Engineering, I.K. Gujral Punjab Technical University, India
Baljeet Kaur
Department of Electronics and Communication Engineering, Guru Nanak Dev Engineering College, India
Marcio Andrey Teixeira
Department of Informatics, Federal Institute of Education, Science, and Technology of São Paulo, Brazil

Abstract


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.

Keywords


IoMT, Security, Smart Watch, IDS

References