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nsl kdd  (New Brunswick Scientific)

 
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    Structured Review

    New Brunswick Scientific nsl kdd
    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
    https://www.bioz.com/product/nsl-kdd/dataset+kdd+nsl/pmc13039109-296-13-23
    Average 86 stars, based on 1 article reviews
    nsl kdd - by Bioz Stars, 2026-10
    86/100 stars

    Images

    1) Product Images from "Conformable Fractional Deep Neural Networks (CFDNN) for high-speed cyber-attack detection"

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

    Journal: Scientific Reports

    doi: 10.1038/s41598-026-45213-w

    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.
    Figure Legend 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.

    Techniques Used:

    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.
    Figure Legend 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.

    Techniques Used: Comparison

    Related Articles

    other:

    Article Title: Detecting lateral movement: A systematic survey
    Article Snippet: 2022 , , 2023 , Univ. of New Brunswick - Canadian Institute of Cyber-defense , NSL-KDD , Popular benchmark dataset in the field of CIS and IoT security. It was produced as an upgraded version of KDD'99, however, it suffers from the lack of public related events..

    Article Title: Prevention and Fighting against Web Attacks through Anomaly Detection Technology. A Systematic Review
    Article Snippet: • NSL-KDD: Created in 2009 by the Information Security Center of Excellence (ISCX), University of New Brunswick (UNB), in order to solve the problem of duplicate records found in KDD Cup 99 [17]; this duplication of records could cause biased results by the learning algorithms, as well as the lack of learning of infrequent records.

    Article Title: Intrusion Detection in IoT Networks Using Deep Learning Algorithm
    Article Snippet: Furthermore, many researchers use network-attack datasets: the knowledge discovery and data (KDD) cup obtained from the International Knowledge Discovery and Data Mining Tools Competition, the NSL-KDD is dataset developed by the Canadian Institute for Cybersecurity at the University of New Brunswick, and the BoT-IoT is dataset developed by the University of New South Wales (UNSW) Canberra Cyber center.

    Article Title: Toward a Lightweight Intrusion Detection System for the Internet of Things
    Article Snippet: Some famous datasets include the NSL-KDD and CICIDS217 both generated by Canadian Institute for Cybersecurity unit based at University of New Brunswick.



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    Image Search Results


    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