Conference
Machine learning-based cyber threat analysis and handling healthcare environments.
| Τίτλος: | Machine learning-based cyber threat analysis and handling healthcare environments. |
|---|---|
| Συγγραφείς: | Chinappan, Sahaya Kingsly, Baby, Abin, Kurian, Anu T., Lalmohan, Arjun Mohan C., Cyriac, Christy |
| Πηγή: | AIP Conference Proceedings; 2025, Vol. 3260 Issue 1, p1-10, 10p |
| Θεματικοί όροι: | Random forest algorithms, Decision trees, Internet security, Electronic health records, Intrusion detection systems (Computer security), Pattern recognition systems, Health care industry, Machine learning |
| Περίληψη: | Cybersecurity issues are becoming more important for Electronic Medical Record (EMR) systems as healthcare facilities become more digital. This research introduces a new method for analyzing and dealing with cyber threats in healthcare institutions by combining the best features of the Random Forest and Decision Tree models. The suggested model improves the efficacy and precision of EMR cyber event detection by combining the best features of the two algorithms. We make use of a large dataset that has been fine-tuned to detect cyber incidents in healthcare settings. This dataset contains a variety of variables of system logs, network traffic, and user behaviors. Torapidly process massive amounts of data and spot patterns that might indicate cyber dangers, the Random Forest algorithm is used. On top of that, rules for recognizing certain kinds of cyber events are provided by Decision Trees. When it comes to detecting cyber threats, the hybrid model outperforms individual algorithms by a wide margin, for accuracy and false positive rates. In addition, the Decision Trees' interpretability improves the model's explainability, which in turn facilitates validation and comprehension of identified cyber risks. Protecting private patient information kept in electronic medical record systems is one area where this study adds to the growing body of knowledge on the need for healthcare cybersecurity. Healthcare businesses may strengthen their security posture with the help of the suggested hybrid model, which offers a solid foundation for proactive cyber threat assessment and response. [ABSTRACT FROM AUTHOR] |
| Copyright of AIP Conference Proceedings is the property of American Institute of Physics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Text: Availability: 0 |
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| Header | DbId: edb DbLabel: Complementary Index An: 186810472 RelevancyScore: 1009 AccessLevel: 6 PubType: Conference PubTypeId: conference PreciseRelevancyScore: 1008.54583740234 |
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| Items | – Name: Title Label: Title Group: Ti Data: Machine learning-based cyber threat analysis and handling healthcare environments. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Chinappan%2C+Sahaya+Kingsly%22">Chinappan, Sahaya Kingsly</searchLink><br /><searchLink fieldCode="AR" term="%22Baby%2C+Abin%22">Baby, Abin</searchLink><br /><searchLink fieldCode="AR" term="%22Kurian%2C+Anu+T%2E%22">Kurian, Anu T.</searchLink><br /><searchLink fieldCode="AR" term="%22Lalmohan%2C+Arjun+Mohan+C%2E%22">Lalmohan, Arjun Mohan C.</searchLink><br /><searchLink fieldCode="AR" term="%22Cyriac%2C+Christy%22">Cyriac, Christy</searchLink> – Name: TitleSource Label: Source Group: Src Data: AIP Conference Proceedings; 2025, Vol. 3260 Issue 1, p1-10, 10p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Random+forest+algorithms%22">Random forest algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+trees%22">Decision trees</searchLink><br /><searchLink fieldCode="DE" term="%22Internet+security%22">Internet security</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+health+records%22">Electronic health records</searchLink><br /><searchLink fieldCode="DE" term="%22Intrusion+detection+systems+%28Computer+security%29%22">Intrusion detection systems (Computer security)</searchLink><br /><searchLink fieldCode="DE" term="%22Pattern+recognition+systems%22">Pattern recognition systems</searchLink><br /><searchLink fieldCode="DE" term="%22Health+care+industry%22">Health care industry</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Cybersecurity issues are becoming more important for Electronic Medical Record (EMR) systems as healthcare facilities become more digital. This research introduces a new method for analyzing and dealing with cyber threats in healthcare institutions by combining the best features of the Random Forest and Decision Tree models. The suggested model improves the efficacy and precision of EMR cyber event detection by combining the best features of the two algorithms. We make use of a large dataset that has been fine-tuned to detect cyber incidents in healthcare settings. This dataset contains a variety of variables of system logs, network traffic, and user behaviors. Torapidly process massive amounts of data and spot patterns that might indicate cyber dangers, the Random Forest algorithm is used. On top of that, rules for recognizing certain kinds of cyber events are provided by Decision Trees. When it comes to detecting cyber threats, the hybrid model outperforms individual algorithms by a wide margin, for accuracy and false positive rates. In addition, the Decision Trees' interpretability improves the model's explainability, which in turn facilitates validation and comprehension of identified cyber risks. Protecting private patient information kept in electronic medical record systems is one area where this study adds to the growing body of knowledge on the need for healthcare cybersecurity. Healthcare businesses may strengthen their security posture with the help of the suggested hybrid model, which offers a solid foundation for proactive cyber threat assessment and response. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of AIP Conference Proceedings is the property of American Institute of Physics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1063/5.0259799 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 1 Subjects: – SubjectFull: Random forest algorithms Type: general – SubjectFull: Decision trees Type: general – SubjectFull: Internet security Type: general – SubjectFull: Electronic health records Type: general – SubjectFull: Intrusion detection systems (Computer security) Type: general – SubjectFull: Pattern recognition systems Type: general – SubjectFull: Health care industry Type: general – SubjectFull: Machine learning Type: general Titles: – TitleFull: Machine learning-based cyber threat analysis and handling healthcare environments. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Chinappan, Sahaya Kingsly – PersonEntity: Name: NameFull: Baby, Abin – PersonEntity: Name: NameFull: Kurian, Anu T. – PersonEntity: Name: NameFull: Lalmohan, Arjun Mohan C. – PersonEntity: Name: NameFull: Cyriac, Christy IsPartOfRelationships: – BibEntity: Dates: – D: 04 M: 04 Text: 2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 0094243X Numbering: – Type: volume Value: 3260 – Type: issue Value: 1 Titles: – TitleFull: AIP Conference Proceedings Type: main |
| ResultId | 1 |