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nsl kdd  (New Brunswick Scientific)
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Fractional order ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha$$\end{document} ) impact analysis on CFDNN performance for <t>(a)</t> <t>NSL-KDD</t> and (b) CIC-IDS2018 datasets. The highlighted region ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1.2 \le \alpha \le 1.8$$\end{document} ) represents the optimal performance range.
Nsl Kdd, supplied by New Brunswick Scientific, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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resource nsl kdd intrusion detection dataset  (Kaggle Inc)
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Fractional order ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha$$\end{document} ) impact analysis on CFDNN performance for <t>(a)</t> <t>NSL-KDD</t> and (b) CIC-IDS2018 datasets. The highlighted region ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1.2 \le \alpha \le 1.8$$\end{document} ) represents the optimal performance range.
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nsl kdd ids dataset  (Kaggle Inc)
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nsl kdd dataset  (New Brunswick Scientific)
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Fractional order ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha$$\end{document} ) impact analysis on CFDNN performance for <t>(a)</t> <t>NSL-KDD</t> and (b) CIC-IDS2018 datasets. The highlighted region ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1.2 \le \alpha \le 1.8$$\end{document} ) represents the optimal performance range.
Nsl Kdd Dataset, supplied by New Brunswick Scientific, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/nsl-kdd/dataset+kdd+nsl/pmc12448997-383-7-3
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nsl kdd dataset - by Bioz Stars, 2026-10
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nsl-kdd dataset  (Mendeley Ltd)
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Mendeley Ltd nsl-kdd dataset
Fractional order ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha$$\end{document} ) impact analysis on CFDNN performance for <t>(a)</t> <t>NSL-KDD</t> and (b) CIC-IDS2018 datasets. The highlighted region ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1.2 \le \alpha \le 1.8$$\end{document} ) represents the optimal performance range.
Nsl Kdd Dataset, supplied by Mendeley Ltd, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Fractional order ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha$$\end{document} ) impact analysis on CFDNN performance for (a) NSL-KDD and (b) CIC-IDS2018 datasets. The highlighted region ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1.2 \le \alpha \le 1.8$$\end{document} ) represents the optimal performance range.

Journal: Scientific Reports

Article Title: Conformable Fractional Deep Neural Networks (CFDNN) for high-speed cyber-attack detection

doi: 10.1038/s41598-026-45213-w

Figure Lengend Snippet: Fractional order ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha$$\end{document} ) impact analysis on CFDNN performance for (a) NSL-KDD and (b) CIC-IDS2018 datasets. The highlighted region ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1.2 \le \alpha \le 1.8$$\end{document} ) represents the optimal performance range.

Article Snippet: The raw data utilized in this study are derived from the publicly available NSL-KDD and CIC-IDS2018 datasets, both accessible through the University of New Brunswick (UNB) repository.

Techniques:

Multi-dimensional performance analysis: (a) NSL-KDD performance radar, (b) CIC-IDS2018 performance radar, (c) Optimal performance comparison at \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha =1.8$$\end{document} , (d) Normalized computational time analysis.

Journal: Scientific Reports

Article Title: Conformable Fractional Deep Neural Networks (CFDNN) for high-speed cyber-attack detection

doi: 10.1038/s41598-026-45213-w

Figure Lengend Snippet: Multi-dimensional performance analysis: (a) NSL-KDD performance radar, (b) CIC-IDS2018 performance radar, (c) Optimal performance comparison at \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha =1.8$$\end{document} , (d) Normalized computational time analysis.

Article Snippet: The raw data utilized in this study are derived from the publicly available NSL-KDD and CIC-IDS2018 datasets, both accessible through the University of New Brunswick (UNB) repository.

Techniques: Comparison