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A Hybrid Framework for Intrusion Detection in Healthcare Systems Using Deep Learning
The unbounded increase in network traffic and user data has made it difficult for network intrusion detection systems to be abreast and perform well. Intrusion Systems are crucial in e-healthcare since the patients' medical records should be kept highly secure, confidential, and accurate. Any c...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8790147/ https://www.ncbi.nlm.nih.gov/pubmed/35096763 http://dx.doi.org/10.3389/fpubh.2021.824898 |
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author | Akshay Kumaar, M. Samiayya, Duraimurugan Vincent, P. M. Durai Raj Srinivasan, Kathiravan Chang, Chuan-Yu Ganesh, Harish |
author_facet | Akshay Kumaar, M. Samiayya, Duraimurugan Vincent, P. M. Durai Raj Srinivasan, Kathiravan Chang, Chuan-Yu Ganesh, Harish |
author_sort | Akshay Kumaar, M. |
collection | PubMed |
description | The unbounded increase in network traffic and user data has made it difficult for network intrusion detection systems to be abreast and perform well. Intrusion Systems are crucial in e-healthcare since the patients' medical records should be kept highly secure, confidential, and accurate. Any change in the actual patient data can lead to errors in the diagnosis and treatment. Most of the existing artificial intelligence-based systems are trained on outdated intrusion detection repositories, which can produce more false positives and require retraining the algorithm from scratch to support new attacks. These processes also make it challenging to secure patient records in medical systems as the intrusion detection mechanisms can become frequently obsolete. This paper proposes a hybrid framework using Deep Learning named “ImmuneNet” to recognize the latest intrusion attacks and defend healthcare data. The proposed framework uses multiple feature engineering processes, oversampling methods to improve class balance, and hyper-parameter optimization techniques to achieve high accuracy and performance. The architecture contains <1 million parameters, making it lightweight, fast, and IoT-friendly, suitable for deploying the IDS on medical devices and healthcare systems. The performance of ImmuneNet was benchmarked against several other machine learning algorithms on the Canadian Institute for Cybersecurity's Intrusion Detection System 2017, 2018, and Bell DNS 2021 datasets which contain extensive real-time and latest cyber attack data. Out of all the experiments, ImmuneNet performed the best on the CIC Bell DNS 2021 dataset with about 99.19% accuracy, 99.22% precision, 99.19% recall, and 99.2% ROC-AUC scores, which are comparatively better and up-to-date than other existing approaches in classifying between requests that are normal, intrusion, and other cyber attacks. |
format | Online Article Text |
id | pubmed-8790147 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-87901472022-01-27 A Hybrid Framework for Intrusion Detection in Healthcare Systems Using Deep Learning Akshay Kumaar, M. Samiayya, Duraimurugan Vincent, P. M. Durai Raj Srinivasan, Kathiravan Chang, Chuan-Yu Ganesh, Harish Front Public Health Public Health The unbounded increase in network traffic and user data has made it difficult for network intrusion detection systems to be abreast and perform well. Intrusion Systems are crucial in e-healthcare since the patients' medical records should be kept highly secure, confidential, and accurate. Any change in the actual patient data can lead to errors in the diagnosis and treatment. Most of the existing artificial intelligence-based systems are trained on outdated intrusion detection repositories, which can produce more false positives and require retraining the algorithm from scratch to support new attacks. These processes also make it challenging to secure patient records in medical systems as the intrusion detection mechanisms can become frequently obsolete. This paper proposes a hybrid framework using Deep Learning named “ImmuneNet” to recognize the latest intrusion attacks and defend healthcare data. The proposed framework uses multiple feature engineering processes, oversampling methods to improve class balance, and hyper-parameter optimization techniques to achieve high accuracy and performance. The architecture contains <1 million parameters, making it lightweight, fast, and IoT-friendly, suitable for deploying the IDS on medical devices and healthcare systems. The performance of ImmuneNet was benchmarked against several other machine learning algorithms on the Canadian Institute for Cybersecurity's Intrusion Detection System 2017, 2018, and Bell DNS 2021 datasets which contain extensive real-time and latest cyber attack data. Out of all the experiments, ImmuneNet performed the best on the CIC Bell DNS 2021 dataset with about 99.19% accuracy, 99.22% precision, 99.19% recall, and 99.2% ROC-AUC scores, which are comparatively better and up-to-date than other existing approaches in classifying between requests that are normal, intrusion, and other cyber attacks. Frontiers Media S.A. 2022-01-12 /pmc/articles/PMC8790147/ /pubmed/35096763 http://dx.doi.org/10.3389/fpubh.2021.824898 Text en Copyright © 2022 Akshay Kumaar, Samiayya, Vincent, Srinivasan, Chang and Ganesh. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Public Health Akshay Kumaar, M. Samiayya, Duraimurugan Vincent, P. M. Durai Raj Srinivasan, Kathiravan Chang, Chuan-Yu Ganesh, Harish A Hybrid Framework for Intrusion Detection in Healthcare Systems Using Deep Learning |
title | A Hybrid Framework for Intrusion Detection in Healthcare Systems Using Deep Learning |
title_full | A Hybrid Framework for Intrusion Detection in Healthcare Systems Using Deep Learning |
title_fullStr | A Hybrid Framework for Intrusion Detection in Healthcare Systems Using Deep Learning |
title_full_unstemmed | A Hybrid Framework for Intrusion Detection in Healthcare Systems Using Deep Learning |
title_short | A Hybrid Framework for Intrusion Detection in Healthcare Systems Using Deep Learning |
title_sort | hybrid framework for intrusion detection in healthcare systems using deep learning |
topic | Public Health |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8790147/ https://www.ncbi.nlm.nih.gov/pubmed/35096763 http://dx.doi.org/10.3389/fpubh.2021.824898 |
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