Large‐Scale Benchmarking of Intrusion Detection Datasets With GPU‐Accelerated Data Pipelines, Complexity Analysis, and Model Evaluation.
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| Title: | Large‐Scale Benchmarking of Intrusion Detection Datasets With GPU‐Accelerated Data Pipelines, Complexity Analysis, and Model Evaluation. |
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| Authors: | Aragão, Marcelo V. C.1 (AUTHOR) marcelovca90@inatel.br, Figueiredo, Felipe A. P. de1 (AUTHOR), Mafra, Samuel B.1 (AUTHOR), Mohanta, Kallol1 (AUTHOR) kmohanta@wiley.com |
| Source: | International Journal of Intelligent Systems. 5/8/2026, Vol. 2026, p1-30. 30p. |
| Subjects: | Intrusion detection systems (Computer security), Data pipelining, Benchmark problems (Computer science), Computational complexity, Machine learning |
| Abstract: | Intrusion detection systems (IDSs) are critical for identifying malicious activity in computer networks; however, the evaluation of machine learning (ML)–based IDS remains inconsistent and fragmented. Many existing studies rely on outdated datasets, neglect computational complexity, or use limited performance metrics. Additionally, few works leverage the full potential of modern graphics processing unit (GPU) acceleration. The objective of this study is to establish a scalable, reproducible, and standardized benchmarking framework for intrusion detection. We present an end‐to‐end, GPU‐accelerated pipeline that integrates automated data preprocessing, intrinsic dataset complexity analysis, and multiobjective hyperparameter optimization (HPO) across more than 70 publicly available datasets. Our numerical findings demonstrate that stratified sampling rates of 10% are sufficient to maintain statistical signal integrity, with class probability deviations remaining below 0.01 relative to the full population. Furthermore, feature‐reduced configurations decrease the model size by a median of 60% while maintaining weighted F1 scores within 0.01 of the baseline. Finally, experimental complexity analysis reveals that the GPU‐accelerated modeling stages achieve empirical time‐invariance (O(1)), reducing training latency by up to two orders of magnitude compared with traditional central processing unit (CPU) workflows. These contributions offer a rigorous quantitative view of the performance‐efficiency trade‐offs essential for next‐generation IDS evaluation. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Intelligent Systems is the property of Wiley-Blackwell 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.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 193626359 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Large‐Scale Benchmarking of Intrusion Detection Datasets With GPU‐Accelerated Data Pipelines, Complexity Analysis, and Model Evaluation. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Aragão%2C+Marcelo+V%2E+C%2E%22">Aragão, Marcelo V. C.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> marcelovca90@inatel.br</i><br /><searchLink fieldCode="AR" term="%22Figueiredo%2C+Felipe+A%2E+P%2E+de%22">Figueiredo, Felipe A. P. de</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Mafra%2C+Samuel+B%2E%22">Mafra, Samuel B.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Mohanta%2C+Kallol%22">Mohanta, Kallol</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> kmohanta@wiley.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Intelligent+Systems%22">International Journal of Intelligent Systems</searchLink>. 5/8/2026, Vol. 2026, p1-30. 30p. – Name: Subject Label: Subjects 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="%22Data+pipelining%22">Data pipelining</searchLink><br /><searchLink fieldCode="DE" term="%22Benchmark+problems+%28Computer+science%29%22">Benchmark problems (Computer science)</searchLink><br /><searchLink fieldCode="DE" term="%22Computational+complexity%22">Computational complexity</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Intrusion detection systems (IDSs) are critical for identifying malicious activity in computer networks; however, the evaluation of machine learning (ML)–based IDS remains inconsistent and fragmented. Many existing studies rely on outdated datasets, neglect computational complexity, or use limited performance metrics. Additionally, few works leverage the full potential of modern graphics processing unit (GPU) acceleration. The objective of this study is to establish a scalable, reproducible, and standardized benchmarking framework for intrusion detection. We present an end‐to‐end, GPU‐accelerated pipeline that integrates automated data preprocessing, intrinsic dataset complexity analysis, and multiobjective hyperparameter optimization (HPO) across more than 70 publicly available datasets. Our numerical findings demonstrate that stratified sampling rates of 10% are sufficient to maintain statistical signal integrity, with class probability deviations remaining below 0.01 relative to the full population. Furthermore, feature‐reduced configurations decrease the model size by a median of 60% while maintaining weighted F1 scores within 0.01 of the baseline. Finally, experimental complexity analysis reveals that the GPU‐accelerated modeling stages achieve empirical time‐invariance (O(1)), reducing training latency by up to two orders of magnitude compared with traditional central processing unit (CPU) workflows. These contributions offer a rigorous quantitative view of the performance‐efficiency trade‐offs essential for next‐generation IDS evaluation. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Intelligent Systems is the property of Wiley-Blackwell 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: 10.1155/int/9925751 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 30 StartPage: 1 Subjects: – SubjectFull: Intrusion detection systems (Computer security) Type: general – SubjectFull: Data pipelining Type: general – SubjectFull: Benchmark problems (Computer science) Type: general – SubjectFull: Computational complexity Type: general – SubjectFull: Machine learning Type: general Titles: – TitleFull: Large‐Scale Benchmarking of Intrusion Detection Datasets With GPU‐Accelerated Data Pipelines, Complexity Analysis, and Model Evaluation. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Aragão, Marcelo V. C. – PersonEntity: Name: NameFull: Figueiredo, Felipe A. P. de – PersonEntity: Name: NameFull: Mafra, Samuel B. – PersonEntity: Name: NameFull: Mohanta, Kallol IsPartOfRelationships: – BibEntity: Dates: – D: 08 M: 05 Text: 5/8/2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 08848173 Numbering: – Type: volume Value: 2026 Titles: – TitleFull: International Journal of Intelligent Systems Type: main |
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