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An Improved Mutual Information Feature Selection Technique for Intrusion Detection Systems in the Internet of Medical Things

In healthcare, the Internet of Things (IoT) is used to remotely monitor patients and provide real-time diagnoses, which is referred to as the Internet of Medical Things (IoMT). This integration poses a risk from cybersecurity threats that can harm patient data and well-being. Hackers can manipulate...

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Autores principales: Alalhareth, Mousa, Hong, Sung-Chul
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10221084/
https://www.ncbi.nlm.nih.gov/pubmed/37430886
http://dx.doi.org/10.3390/s23104971
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author Alalhareth, Mousa
Hong, Sung-Chul
author_facet Alalhareth, Mousa
Hong, Sung-Chul
author_sort Alalhareth, Mousa
collection PubMed
description In healthcare, the Internet of Things (IoT) is used to remotely monitor patients and provide real-time diagnoses, which is referred to as the Internet of Medical Things (IoMT). This integration poses a risk from cybersecurity threats that can harm patient data and well-being. Hackers can manipulate biometric data from biosensors or disrupt the IoMT system, which is a major concern. To address this issue, intrusion detection systems (IDS) have been proposed, particularly using deep learning algorithms. However, developing IDS for IoMT is challenging due to high data dimensionality leading to model overfitting and degraded detection accuracy. Feature selection has been proposed to prevent overfitting, but the existing methods assume that feature redundancy increases linearly with the size of the selected features. Such an assumption does not hold, as the amount of information a feature carries about the attack pattern varies from feature to feature, especially when dealing with early patterns, due to data sparsity that makes it difficult to perceive the common characteristics of selected features. This negatively affects the ability of the mutual information feature selection (MIFS) goal function to estimate the redundancy coefficient accurately. To overcome this issue, this paper proposes an enhanced feature selection technique called Logistic Redundancy Coefficient Gradual Upweighting MIFS (LRGU-MIFS) that evaluates candidate features individually instead of comparing them with common characteristics of the already-selected features. Unlike the existing feature selection techniques, LRGU calculates the redundancy score of a feature using the logistic function. It increases the redundancy value based on the logistic curve, which reflects the nonlinearity of the relationship of the mutual information between features in the selected set. Then, the LRGU was incorporated into the goal function of MIFS as a redundancy coefficient. The experimental evaluation shows that the proposed LRGU was able to identify a compact set of significant features that outperformed those selected by the existing techniques. The proposed technique overcomes the challenge of perceiving common characteristics in cases of insufficient attack patterns and outperforms existing techniques in identifying significant features.
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spelling pubmed-102210842023-05-28 An Improved Mutual Information Feature Selection Technique for Intrusion Detection Systems in the Internet of Medical Things Alalhareth, Mousa Hong, Sung-Chul Sensors (Basel) Article In healthcare, the Internet of Things (IoT) is used to remotely monitor patients and provide real-time diagnoses, which is referred to as the Internet of Medical Things (IoMT). This integration poses a risk from cybersecurity threats that can harm patient data and well-being. Hackers can manipulate biometric data from biosensors or disrupt the IoMT system, which is a major concern. To address this issue, intrusion detection systems (IDS) have been proposed, particularly using deep learning algorithms. However, developing IDS for IoMT is challenging due to high data dimensionality leading to model overfitting and degraded detection accuracy. Feature selection has been proposed to prevent overfitting, but the existing methods assume that feature redundancy increases linearly with the size of the selected features. Such an assumption does not hold, as the amount of information a feature carries about the attack pattern varies from feature to feature, especially when dealing with early patterns, due to data sparsity that makes it difficult to perceive the common characteristics of selected features. This negatively affects the ability of the mutual information feature selection (MIFS) goal function to estimate the redundancy coefficient accurately. To overcome this issue, this paper proposes an enhanced feature selection technique called Logistic Redundancy Coefficient Gradual Upweighting MIFS (LRGU-MIFS) that evaluates candidate features individually instead of comparing them with common characteristics of the already-selected features. Unlike the existing feature selection techniques, LRGU calculates the redundancy score of a feature using the logistic function. It increases the redundancy value based on the logistic curve, which reflects the nonlinearity of the relationship of the mutual information between features in the selected set. Then, the LRGU was incorporated into the goal function of MIFS as a redundancy coefficient. The experimental evaluation shows that the proposed LRGU was able to identify a compact set of significant features that outperformed those selected by the existing techniques. The proposed technique overcomes the challenge of perceiving common characteristics in cases of insufficient attack patterns and outperforms existing techniques in identifying significant features. MDPI 2023-05-22 /pmc/articles/PMC10221084/ /pubmed/37430886 http://dx.doi.org/10.3390/s23104971 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Alalhareth, Mousa
Hong, Sung-Chul
An Improved Mutual Information Feature Selection Technique for Intrusion Detection Systems in the Internet of Medical Things
title An Improved Mutual Information Feature Selection Technique for Intrusion Detection Systems in the Internet of Medical Things
title_full An Improved Mutual Information Feature Selection Technique for Intrusion Detection Systems in the Internet of Medical Things
title_fullStr An Improved Mutual Information Feature Selection Technique for Intrusion Detection Systems in the Internet of Medical Things
title_full_unstemmed An Improved Mutual Information Feature Selection Technique for Intrusion Detection Systems in the Internet of Medical Things
title_short An Improved Mutual Information Feature Selection Technique for Intrusion Detection Systems in the Internet of Medical Things
title_sort improved mutual information feature selection technique for intrusion detection systems in the internet of medical things
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10221084/
https://www.ncbi.nlm.nih.gov/pubmed/37430886
http://dx.doi.org/10.3390/s23104971
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