Analysis of IoMT data sources.
<div><p>The rapid development and integration of interconnected healthcare devices and communication networks within the Internet of Medical Things (IoMT) have significantly enhanced healthcare services. However, this growth has also introduced new vulnerabilities, increasing the risk of...
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| مؤلفون آخرون: | , , , , , |
| منشور في: |
2025
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| _version_ | 1852020466572591104 |
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| author | Mohanad Faeq Ali (21354273) |
| author2 | Mohammed Shakir Mohmood (21354276) Ban Salman Shukur (21354279) Rex Bacarra (21354282) Jamil Abedalrahim Jamil Alsayaydeh (21354285) Masrullizam Mat Ibrahim (21354288) Safarudin Gazali Herawan (19749996) |
| author2_role | author author author author author author |
| author_facet | Mohanad Faeq Ali (21354273) Mohammed Shakir Mohmood (21354276) Ban Salman Shukur (21354279) Rex Bacarra (21354282) Jamil Abedalrahim Jamil Alsayaydeh (21354285) Masrullizam Mat Ibrahim (21354288) Safarudin Gazali Herawan (19749996) |
| author_role | author |
| dc.creator.none.fl_str_mv | Mohanad Faeq Ali (21354273) Mohammed Shakir Mohmood (21354276) Ban Salman Shukur (21354279) Rex Bacarra (21354282) Jamil Abedalrahim Jamil Alsayaydeh (21354285) Masrullizam Mat Ibrahim (21354288) Safarudin Gazali Herawan (19749996) |
| dc.date.none.fl_str_mv | 2025-05-13T23:18:58Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0321941.g001 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Analysis_of_IoMT_data_sources_/29056644 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biotechnology Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified recursive feature elimination outperformed existing models model &# 8217 maximum recognition rate including false positive implemented using python identifying abnormal patterns false positive rate false negative rates false negative rate causes prediction inaccuracies computational complexity issues communication networks within indicate potential cyberattacks iomt threat detection dynamic iomt ecosystem recurrent neural networks interconnected healthcare devices enhancing cyberattack prediction dataset availability issues computational efficiency enhances sensitive healthcare data recurrent networks healthcare devices computational efficiency detecting cyberattacks efficiency depends iomt infrastructure iomt environments xlink "> various metrics training data rfe ), results demonstrated research introduces research focuses rapid development raising concerns principal component optimization technique medical things learning techniques irrelevant features innovative machine gathered information extracted features evolving threats cybersecurity attacks created system attacks threaten |
| dc.title.none.fl_str_mv | Analysis of IoMT data sources. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <div><p>The rapid development and integration of interconnected healthcare devices and communication networks within the Internet of Medical Things (IoMT) have significantly enhanced healthcare services. However, this growth has also introduced new vulnerabilities, increasing the risk of cybersecurity attacks. These attacks threaten the confidentiality, integrity, and availability of sensitive healthcare data, raising concerns about the reliability of IoMT infrastructure. Addressing these challenges requires advanced cybersecurity measures to protect the dynamic IoMT ecosystem from evolving threats. This research focuses on enhancing cyberattack prediction and prevention in IoMT environments through innovative Machine-learning techniques to improve healthcare data security and resilience. However, the existing model’s efficiency depends on the diversity of data, which leads to computational complexity issues. In addition, the conventional model faces overfitting issues in training data, which causes prediction inaccuracies. Thus, the research introduces the hybridized cyber attack prediction model (HCAP) and analyzes various IoMT data source information to address the limitations of dataset availability issues. The gathered information is processed with the help of Principal Component-Recursive Feature Elimination (PC-RFE), which eliminates the irrelevant features. The extracted features are fed into the lion-optimization technique to fine-tune the hyperparameters of the recurrent neural networks, enhancing the model’s ability to efficiently predict cybersecurity threats with a maximum recognition rate in IoMT environments. The recurrent networks, specifically Long Short Term Memory (LSTM), process data from healthcare devices, identifying abnormal patterns that indicate potential cyberattacks over time. The created system was implemented using Python, and various metrics, including false positive and false negative rates, accuracy, precision, recall, and computational efficiency, were used for evaluation. The results demonstrated that the proposed HCAP model achieved 98% accuracy in detecting cyberattacks and outperformed existing models, reducing the false positive rate by 25%. The false negative rate by 20% and a 30% improvement in computational efficiency enhances the reliability of IoMT threat detection in healthcare applications.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_1f85da44bf147c8ae942b06d7faa435e |
| identifier_str_mv | 10.1371/journal.pone.0321941.g001 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/29056644 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Analysis of IoMT data sources.Mohanad Faeq Ali (21354273)Mohammed Shakir Mohmood (21354276)Ban Salman Shukur (21354279)Rex Bacarra (21354282)Jamil Abedalrahim Jamil Alsayaydeh (21354285)Masrullizam Mat Ibrahim (21354288)Safarudin Gazali Herawan (19749996)BiotechnologySpace ScienceBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedrecursive feature eliminationoutperformed existing modelsmodel &# 8217maximum recognition rateincluding false positiveimplemented using pythonidentifying abnormal patternsfalse positive ratefalse negative ratesfalse negative ratecauses prediction inaccuraciescomputational complexity issuescommunication networks withinindicate potential cyberattacksiomt threat detectiondynamic iomt ecosystemrecurrent neural networksinterconnected healthcare devicesenhancing cyberattack predictiondataset availability issuescomputational efficiency enhancessensitive healthcare datarecurrent networkshealthcare devicescomputational efficiencydetecting cyberattacksefficiency dependsiomt infrastructureiomt environmentsxlink ">various metricstraining datarfe ),results demonstratedresearch introducesresearch focusesrapid developmentraising concernsprincipal componentoptimization techniquemedical thingslearning techniquesirrelevant featuresinnovative machinegathered informationextracted featuresevolving threatscybersecurity attackscreated systemattacks threaten<div><p>The rapid development and integration of interconnected healthcare devices and communication networks within the Internet of Medical Things (IoMT) have significantly enhanced healthcare services. However, this growth has also introduced new vulnerabilities, increasing the risk of cybersecurity attacks. These attacks threaten the confidentiality, integrity, and availability of sensitive healthcare data, raising concerns about the reliability of IoMT infrastructure. Addressing these challenges requires advanced cybersecurity measures to protect the dynamic IoMT ecosystem from evolving threats. This research focuses on enhancing cyberattack prediction and prevention in IoMT environments through innovative Machine-learning techniques to improve healthcare data security and resilience. However, the existing model’s efficiency depends on the diversity of data, which leads to computational complexity issues. In addition, the conventional model faces overfitting issues in training data, which causes prediction inaccuracies. Thus, the research introduces the hybridized cyber attack prediction model (HCAP) and analyzes various IoMT data source information to address the limitations of dataset availability issues. The gathered information is processed with the help of Principal Component-Recursive Feature Elimination (PC-RFE), which eliminates the irrelevant features. The extracted features are fed into the lion-optimization technique to fine-tune the hyperparameters of the recurrent neural networks, enhancing the model’s ability to efficiently predict cybersecurity threats with a maximum recognition rate in IoMT environments. The recurrent networks, specifically Long Short Term Memory (LSTM), process data from healthcare devices, identifying abnormal patterns that indicate potential cyberattacks over time. The created system was implemented using Python, and various metrics, including false positive and false negative rates, accuracy, precision, recall, and computational efficiency, were used for evaluation. The results demonstrated that the proposed HCAP model achieved 98% accuracy in detecting cyberattacks and outperformed existing models, reducing the false positive rate by 25%. The false negative rate by 20% and a 30% improvement in computational efficiency enhances the reliability of IoMT threat detection in healthcare applications.</p></div>2025-05-13T23:18:58ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0321941.g001https://figshare.com/articles/figure/Analysis_of_IoMT_data_sources_/29056644CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/290566442025-05-13T23:18:58Z |
| spellingShingle | Analysis of IoMT data sources. Mohanad Faeq Ali (21354273) Biotechnology Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified recursive feature elimination outperformed existing models model &# 8217 maximum recognition rate including false positive implemented using python identifying abnormal patterns false positive rate false negative rates false negative rate causes prediction inaccuracies computational complexity issues communication networks within indicate potential cyberattacks iomt threat detection dynamic iomt ecosystem recurrent neural networks interconnected healthcare devices enhancing cyberattack prediction dataset availability issues computational efficiency enhances sensitive healthcare data recurrent networks healthcare devices computational efficiency detecting cyberattacks efficiency depends iomt infrastructure iomt environments xlink "> various metrics training data rfe ), results demonstrated research introduces research focuses rapid development raising concerns principal component optimization technique medical things learning techniques irrelevant features innovative machine gathered information extracted features evolving threats cybersecurity attacks created system attacks threaten |
| status_str | publishedVersion |
| title | Analysis of IoMT data sources. |
| title_full | Analysis of IoMT data sources. |
| title_fullStr | Analysis of IoMT data sources. |
| title_full_unstemmed | Analysis of IoMT data sources. |
| title_short | Analysis of IoMT data sources. |
| title_sort | Analysis of IoMT data sources. |
| topic | Biotechnology Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified recursive feature elimination outperformed existing models model &# 8217 maximum recognition rate including false positive implemented using python identifying abnormal patterns false positive rate false negative rates false negative rate causes prediction inaccuracies computational complexity issues communication networks within indicate potential cyberattacks iomt threat detection dynamic iomt ecosystem recurrent neural networks interconnected healthcare devices enhancing cyberattack prediction dataset availability issues computational efficiency enhances sensitive healthcare data recurrent networks healthcare devices computational efficiency detecting cyberattacks efficiency depends iomt infrastructure iomt environments xlink "> various metrics training data rfe ), results demonstrated research introduces research focuses rapid development raising concerns principal component optimization technique medical things learning techniques irrelevant features innovative machine gathered information extracted features evolving threats cybersecurity attacks created system attacks threaten |