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Many existing studies rely on outdated datasets, neglect computational complexity, or use limited performance metrics.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\u2010to\u2010end, GPU\u2010accelerated 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\u2010reduced configurations decrease the model size by a median of 60% while maintaining weighted\n                    <jats:italic>F<\/jats:italic>\n                    <jats:sub>1<\/jats:sub>\n                    scores within 0.01 of the baseline. Finally, experimental complexity analysis reveals that the GPU\u2010accelerated modeling stages achieve empirical time\u2010invariance (\n                    <jats:italic>O<\/jats:italic>\n                    (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\u2010efficiency trade\u2010offs essential for next\u2010generation IDS evaluation.\n                  <\/jats:p>","DOI":"10.1155\/int\/9925751","type":"journal-article","created":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T06:42:33Z","timestamp":1778308953000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Large\u2010Scale Benchmarking of Intrusion Detection Datasets With GPU\u2010Accelerated Data Pipelines, Complexity Analysis, and Model Evaluation"],"prefix":"10.1155","volume":"2026","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8999-8169","authenticated-orcid":false,"given":"Marcelo V. C.","family":"Arag\u00e3o","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2167-7286","authenticated-orcid":false,"given":"Felipe A. 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