Academic Journal
Performance Enhancement of Intrusion Detection System in Cloud by Using Boruta Algorithm.
| Τίτλος: | Performance Enhancement of Intrusion Detection System in Cloud by Using Boruta Algorithm. |
|---|---|
| Συγγραφείς: | Lifandali, Oumaima, Chiba, Zouhair, Abghour, Noreddine, Moussaid, Khalid, Miyara, Mounia, Ouaguid, Abdellah |
| Πηγή: | ACM Transactions on Privacy & Security; Aug2025, Vol. 28 Issue 3, p1-33, 33p |
| Θεματικοί όροι: | Intrusion detection systems (Computer security), Cloud computing, Data reduction, Feature selection, K-nearest neighbor classification, Classification algorithms, Data security, Naive Bayes classification |
| Περίληψη: | Presently, cloud computing stands as a dependable choice for enterprises seeking contemporary, adaptable IT solutions capable of managing vast volumes of business data. Its adoption holds the promise of enhancing operational efficiency and productivity. However, cloud computing remains a dynamic and evolving technology landscape, fraught with inherent security challenges. Malevolent actors perpetually scour for novel methodologies to compromise the integrity of data hosted within cloud environments. For instance, data theft, achieved through downloading or encrypting sensitive information, and Distributed Denial of Service (DDoS) assaults targeting cloud infrastructures, pose persistent threats. To address these pressing concerns, the solution outlined in this article advocates for intrusion detection within cloud environments employing a plethora of classification algorithms. To ensure the precision of outcomes, the proposed approach incorporates the meticulous selection of pertinent attributes from the dataset, leveraging the Boruta algorithm. Our research has demonstrated that combining Boruta with classifiers yields impressive results, achieving a recall of 100% with KNN on the CICIDS 2017 dataset and a precision of 100% with Naive Bayes on the CICDDOS 2019 dataset. These results underscore the significant role of feature selection in enhancing detection performance, affirming its importance for achieving optimal results in intrusion detection systems. [ABSTRACT FROM AUTHOR] |
| Copyright of ACM Transactions on Privacy & Security is the property of Association for Computing Machinery 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 CustomLinks: – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:edb&genre=article&issn=24712566&ISBN=&volume=28&issue=3&date=20250801&spage=1&pages=1-33&title=ACM Transactions on Privacy & Security&atitle=Performance%20Enhancement%20of%20Intrusion%20Detection%20System%20in%20Cloud%20by%20Using%20Boruta%20Algorithm.&aulast=Lifandali%2C%20Oumaima&id=DOI:10.1145/3736761 Name: Full Text Finder (for New FTF UI) (ns324271) Category: fullText Text: Full Text Finder MouseOverText: Full Text Finder |
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| Items | – Name: Title Label: Title Group: Ti Data: Performance Enhancement of Intrusion Detection System in Cloud by Using Boruta Algorithm. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Lifandali%2C+Oumaima%22">Lifandali, Oumaima</searchLink><br /><searchLink fieldCode="AR" term="%22Chiba%2C+Zouhair%22">Chiba, Zouhair</searchLink><br /><searchLink fieldCode="AR" term="%22Abghour%2C+Noreddine%22">Abghour, Noreddine</searchLink><br /><searchLink fieldCode="AR" term="%22Moussaid%2C+Khalid%22">Moussaid, Khalid</searchLink><br /><searchLink fieldCode="AR" term="%22Miyara%2C+Mounia%22">Miyara, Mounia</searchLink><br /><searchLink fieldCode="AR" term="%22Ouaguid%2C+Abdellah%22">Ouaguid, Abdellah</searchLink> – Name: TitleSource Label: Source Group: Src Data: ACM Transactions on Privacy & Security; Aug2025, Vol. 28 Issue 3, p1-33, 33p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Intrusion+detection+systems+%28Computer+security%29%22">Intrusion detection systems (Computer security)</searchLink><br /><searchLink fieldCode="DE" term="%22Cloud+computing%22">Cloud computing</searchLink><br /><searchLink fieldCode="DE" term="%22Data+reduction%22">Data reduction</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+selection%22">Feature selection</searchLink><br /><searchLink fieldCode="DE" term="%22K-nearest+neighbor+classification%22">K-nearest neighbor classification</searchLink><br /><searchLink fieldCode="DE" term="%22Classification+algorithms%22">Classification algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Data+security%22">Data security</searchLink><br /><searchLink fieldCode="DE" term="%22Naive+Bayes+classification%22">Naive Bayes classification</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Presently, cloud computing stands as a dependable choice for enterprises seeking contemporary, adaptable IT solutions capable of managing vast volumes of business data. Its adoption holds the promise of enhancing operational efficiency and productivity. However, cloud computing remains a dynamic and evolving technology landscape, fraught with inherent security challenges. Malevolent actors perpetually scour for novel methodologies to compromise the integrity of data hosted within cloud environments. For instance, data theft, achieved through downloading or encrypting sensitive information, and Distributed Denial of Service (DDoS) assaults targeting cloud infrastructures, pose persistent threats. To address these pressing concerns, the solution outlined in this article advocates for intrusion detection within cloud environments employing a plethora of classification algorithms. To ensure the precision of outcomes, the proposed approach incorporates the meticulous selection of pertinent attributes from the dataset, leveraging the Boruta algorithm. Our research has demonstrated that combining Boruta with classifiers yields impressive results, achieving a recall of 100% with KNN on the CICIDS 2017 dataset and a precision of 100% with Naive Bayes on the CICDDOS 2019 dataset. These results underscore the significant role of feature selection in enhancing detection performance, affirming its importance for achieving optimal results in intrusion detection systems. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of ACM Transactions on Privacy & Security is the property of Association for Computing Machinery 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: 10.1145/3736761 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 33 StartPage: 1 Subjects: – SubjectFull: Intrusion detection systems (Computer security) Type: general – SubjectFull: Cloud computing Type: general – SubjectFull: Data reduction Type: general – SubjectFull: Feature selection Type: general – SubjectFull: K-nearest neighbor classification Type: general – SubjectFull: Classification algorithms Type: general – SubjectFull: Data security Type: general – SubjectFull: Naive Bayes classification Type: general Titles: – TitleFull: Performance Enhancement of Intrusion Detection System in Cloud by Using Boruta Algorithm. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Lifandali, Oumaima – PersonEntity: Name: NameFull: Chiba, Zouhair – PersonEntity: Name: NameFull: Abghour, Noreddine – PersonEntity: Name: NameFull: Moussaid, Khalid – PersonEntity: Name: NameFull: Miyara, Mounia – PersonEntity: Name: NameFull: Ouaguid, Abdellah IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: Aug2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 24712566 Numbering: – Type: volume Value: 28 – Type: issue Value: 3 Titles: – TitleFull: ACM Transactions on Privacy & Security Type: main |
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