2 datasets found
  1. CIC-IDS-2017 V2

    • zenodo.org
    zip
    Updated Nov 26, 2024
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    Akshayraj Madhubalan; Akshayraj Madhubalan; Amit Gautam; Amit Gautam; Priya Tiwary; Priya Tiwary (2024). CIC-IDS-2017 V2 [Dataset]. http://doi.org/10.5281/zenodo.10141593
    Explore at:
    zipAvailable download formats
    Dataset updated
    Nov 26, 2024
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Akshayraj Madhubalan; Akshayraj Madhubalan; Amit Gautam; Amit Gautam; Priya Tiwary; Priya Tiwary
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The CIC-IDS-V2 is an extended version of the original CIC-IDS 2017 dataset. The dataset is normalised and 1 new class called "Comb" is added which is a combination of synthesised data of multiple non-benign classes.

    To cite the dataset, please reference the original paper with DOI: 10.1109/SmartNets61466.2024.10577645. The paper is published in IEEE SmartNets and can be accessed here.

    Citation info:

    Madhubalan, Akshayraj & Gautam, Amit & Tiwary, Priya. (2024). Blender-GAN: Multi-Target Conditional Generative Adversarial Network for Novel Class Synthetic Data Generation. 1-7. 10.1109/SmartNets61466.2024.10577645.

    This dataset was made by Abluva Inc, a Palo Alto based, research-driven Data Protection firm. Our data protection platform empowers customers to secure data through advanced security mechanisms such as Fine Grained Access control and sophisticated depersonalization algorithms (e.g. Pseudonymization, Anonymization and Randomization). Abluva's Data Protection solutions facilitate data democratization within and outside the organizations, mitigating the concerns related to theft and compliance. The innovative intrusion detection algorithm by Abluva employs patented technologies for an intricately balanced approach that excludes normal access deviations, ensuring intrusion detection without disrupting the business operations. Abluva’s Solution enables organizations to extract further value from their data by enabling secure Knowledge Graphs and deploying Secure Data as a Service among other novel uses of data. Committed to providing a safe and secure environment, Abluva empowers organizations to unlock the full potential of their data.

  2. h

    CIC-IDS-2017-V2

    • huggingface.co
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    Abluva Inc, CIC-IDS-2017-V2 [Dataset]. https://huggingface.co/datasets/abluva/CIC-IDS-2017-V2
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset authored and provided by
    Abluva Inc
    License

    Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
    License information was derived automatically

    Description

    The CIC-IDS-V2 is an extended version of the original CIC-IDS 2017 dataset. The dataset is normalised and 1 new class called "Comb" is added which is a combination of synthesised data of multiple non-benign classes. To cite the dataset, please reference the original paper with DOI: 10.1109/SmartNets61466.2024.10577645. The paper is published in IEEE SmartNets and can be accessed here:… See the full description on the dataset page: https://huggingface.co/datasets/abluva/CIC-IDS-2017-V2.

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Share
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TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
Akshayraj Madhubalan; Akshayraj Madhubalan; Amit Gautam; Amit Gautam; Priya Tiwary; Priya Tiwary (2024). CIC-IDS-2017 V2 [Dataset]. http://doi.org/10.5281/zenodo.10141593
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CIC-IDS-2017 V2

Explore at:
zipAvailable download formats
Dataset updated
Nov 26, 2024
Dataset provided by
Zenodohttp://zenodo.org/
Authors
Akshayraj Madhubalan; Akshayraj Madhubalan; Amit Gautam; Amit Gautam; Priya Tiwary; Priya Tiwary
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Description

The CIC-IDS-V2 is an extended version of the original CIC-IDS 2017 dataset. The dataset is normalised and 1 new class called "Comb" is added which is a combination of synthesised data of multiple non-benign classes.

To cite the dataset, please reference the original paper with DOI: 10.1109/SmartNets61466.2024.10577645. The paper is published in IEEE SmartNets and can be accessed here.

Citation info:

Madhubalan, Akshayraj & Gautam, Amit & Tiwary, Priya. (2024). Blender-GAN: Multi-Target Conditional Generative Adversarial Network for Novel Class Synthetic Data Generation. 1-7. 10.1109/SmartNets61466.2024.10577645.

This dataset was made by Abluva Inc, a Palo Alto based, research-driven Data Protection firm. Our data protection platform empowers customers to secure data through advanced security mechanisms such as Fine Grained Access control and sophisticated depersonalization algorithms (e.g. Pseudonymization, Anonymization and Randomization). Abluva's Data Protection solutions facilitate data democratization within and outside the organizations, mitigating the concerns related to theft and compliance. The innovative intrusion detection algorithm by Abluva employs patented technologies for an intricately balanced approach that excludes normal access deviations, ensuring intrusion detection without disrupting the business operations. Abluva’s Solution enables organizations to extract further value from their data by enabling secure Knowledge Graphs and deploying Secure Data as a Service among other novel uses of data. Committed to providing a safe and secure environment, Abluva empowers organizations to unlock the full potential of their data.

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