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 Many existing studies rely on outdated datasets, neglect computational complexity, or use limited performance metrics.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] |
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| Database: | Engineering Source |
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| 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] |
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| ISSN: | 08848173 |
| DOI: | 10.1155/int/9925751 |
