59 datasets found
  1. i

    CICIDS2017 and UNBSW-NB15

    • ieee-dataport.org
    Updated Dec 13, 2023
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    xinpeng chen (2023). CICIDS2017 and UNBSW-NB15 [Dataset]. https://ieee-dataport.org/documents/cicids2017-and-unbsw-nb15
    Explore at:
    Dataset updated
    Dec 13, 2023
    Authors
    xinpeng chen
    License

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

    Description

    DoS

  2. f

    CIC-IDS2017 result.

    • plos.figshare.com
    xls
    Updated Jun 21, 2024
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    Nasrullah Khan; Muhammad Ismail Mohmand; Sadaqat ur Rehman; Zia Ullah; Zahid Khan; Wadii Boulila (2024). CIC-IDS2017 result. [Dataset]. http://doi.org/10.1371/journal.pone.0299666.t007
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    xlsAvailable download formats
    Dataset updated
    Jun 21, 2024
    Dataset provided by
    PLOS ONE
    Authors
    Nasrullah Khan; Muhammad Ismail Mohmand; Sadaqat ur Rehman; Zia Ullah; Zahid Khan; Wadii Boulila
    License

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

    Description

    Computer networks face vulnerability to numerous attacks, which pose significant threats to our data security and the freedom of communication. This paper introduces a novel intrusion detection technique that diverges from traditional methods by leveraging Recurrent Neural Networks (RNNs) for both data preprocessing and feature extraction. The proposed process is based on the following steps: (1) training the data using RNNs, (2) extracting features from their hidden layers, and (3) applying various classification algorithms. This methodology offers significant advantages and greatly differs from existing intrusion detection practices. The effectiveness of our method is demonstrated through trials on the Network Security Laboratory (NSL) and Canadian Institute for Cybersecurity (CIC) 2017 datasets, where the application of RNNs for intrusion detection shows substantial practical implications. Specifically, we achieved accuracy scores of 99.6% with Decision Tree, Random Forest, and CatBoost classifiers on the NSL dataset, and 99.8% and 99.9%, respectively, on the CIC 2017 dataset. By reversing the conventional sequence of training data with RNNs and then extracting features before applying classification algorithms, our approach provides a major shift in intrusion detection methodologies. This modification in the pipeline underscores the benefits of utilizing RNNs for feature extraction and data preprocessing, meeting the critical need to safeguard data security and communication freedom against ever-evolving network threats.

  3. h

    CIC-IDS-2017

    • huggingface.co
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    makekali, CIC-IDS-2017 [Dataset]. https://huggingface.co/datasets/makekali/CIC-IDS-2017
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    Authors
    makekali
    License

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

    Description

    makekali/CIC-IDS-2017 dataset hosted on Hugging Face and contributed by the HF Datasets community

  4. o

    UNSW-NB15 and CIC-IDS2017 Labelled PCAP Data

    • explore.openaire.eu
    • zenodo.org
    Updated Oct 27, 2022
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    Yasir Ali Farrukh Farrukh; Irfan Khan; Syed Wali; David Bierbrauer; John A Pavlik; Nathaniel D. Bastian (2022). UNSW-NB15 and CIC-IDS2017 Labelled PCAP Data [Dataset]. http://doi.org/10.5281/zenodo.7258579
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    Dataset updated
    Oct 27, 2022
    Authors
    Yasir Ali Farrukh Farrukh; Irfan Khan; Syed Wali; David Bierbrauer; John A Pavlik; Nathaniel D. Bastian
    Description

    Packet Capture (PCAP) files of UNSW-NB15 and CIC-IDS2017 dataset are processed and labelled utilizing the CSV files. Each packet is labelled by comparing the eight distinct features: Source IP, Destination IP, Source Port, Destination Port, Starting time, Ending time, Protocol and Time to live. The dimensions for the dataset is Nx1504. All column of the dataset are integers, therefore you can directly utilize this dataset in you machine learning models. Moreover, details of the whole processing and transformation is provided in the following GitHub Repo: https://github.com/Yasir-ali-farrukh/Payload-Byte You can utilize the tool available at the above mentioned GitHub repo to generate labelled dataset from scratch. All of the detail of processing and transformation is provided in the following paper: ```yaml @article{Payload, author = "Yasir Ali Farrukh and Irfan Khan and Syed Wali and David Bierbrauer and Nathaniel Bastian", title = "{Payload-Byte: A Tool for Extracting and Labeling Packet Capture Files of Modern Network Intrusion Detection Datasets}", year = "2022", month = "9", url = "https://www.techrxiv.org/articles/preprint/Payload-Byte_A_Tool_for_Extracting_and_Labeling_Packet_Capture_Files_of_Modern_Network_Intrusion_Detection_Datasets/20714221", doi = "10.36227/techrxiv.20714221.v1" }

  5. h

    cyberbert_dataset

    • huggingface.co
    Updated Apr 10, 2025
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    Chaitany Agrawal (2025). cyberbert_dataset [Dataset]. https://huggingface.co/datasets/agrawalchaitany/cyberbert_dataset
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    Dataset updated
    Apr 10, 2025
    Authors
    Chaitany Agrawal
    License

    https://choosealicense.com/licenses/other/https://choosealicense.com/licenses/other/

    Description

    Cleaned CICIDS2017 Dataset

    This dataset is a cleaned and preprocessed version of the CICIDS2017 dataset created by the Canadian Institute for Cybersecurity, University of New Brunswick.

      Modifications
    

    Removed duplicate records Normalized feature names Filtered specific attack types Piviot the different attack data into single dataset

      Source
    

    Original dataset: CICIDS2017

      License & Citation
    

    This dataset is provided for research purposes. Please referโ€ฆ See the full description on the dataset page: https://huggingface.co/datasets/agrawalchaitany/cyberbert_dataset.

  6. 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:โ€ฆ See the full description on the dataset page: https://huggingface.co/datasets/abluva/CIC-IDS-2017-V2.

  7. m

    CIC-DDoS2019 Dataset

    • data.mendeley.com
    Updated Mar 3, 2023
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    Md Alamin Talukder (2023). CIC-DDoS2019 Dataset [Dataset]. http://doi.org/10.17632/ssnc74xm6r.1
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    Dataset updated
    Mar 3, 2023
    Authors
    Md Alamin Talukder
    License

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

    Description

    Distributed Denial of Service (DDoS) attack is a menace to network security that aims at exhausting the target networks with malicious traffic. Although many statistical methods have been designed for DDoS attack detection, designing a real-time detector with low computational overhead is still one of the main concerns. On the other hand, the evaluation of new detection algorithms and techniques heavily relies on the existence of well-designed datasets. In this paper, first, we review the existing datasets comprehensively and propose a new taxonomy for DDoS attacks. Secondly, we generate a new dataset, namely CICDDoS2019, which remedies all current shortcomings. Thirdly, using the generated dataset, we propose a new detection and family classification approach based on a set of network flow features. Finally, we provide the most important feature sets to detect different types of DDoS attacks with their corresponding weights.

    The dataset offers an extended set of Distributed Denial of Service attacks, most of which employ some form of amplification through reflection. The dataset shares its feature set with the other CIC NIDS datasets, IDS2017, IDS2018 and DoS2017

    original paper link: https://ieeexplore.ieee.org/abstract/document/8888419 kaggle dataset link: https://www.kaggle.com/datasets/dhoogla/cicddos2019

  8. f

    Network Intrusion Detection Datasets

    • figshare.com
    txt
    Updated May 30, 2023
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    Ogobuchi Daniel Okey; Demostenes Zegarra Rodriguez (2023). Network Intrusion Detection Datasets [Dataset]. http://doi.org/10.6084/m9.figshare.23118164.v1
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    txtAvailable download formats
    Dataset updated
    May 30, 2023
    Dataset provided by
    figshare
    Authors
    Ogobuchi Daniel Okey; Demostenes Zegarra Rodriguez
    License

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

    Description

    With the continuous expansion of data exchange, the threat of cybercrime and network invasions is also on the rise. This project aims to address these concerns by investigating an innovative approach: an Attentive Transformer Deep Learning Algorithm for Intrusion Detection of IoT Systems using Automatic Xplainable Feature Selection. The primary focus of this project is to develop an effective Intrusion Detection System (IDS) using the aforementioned algorithm. To accomplish this, carefully curated datasets have been utilized, which have been created through a meticulous process involving data extraction from the University of New Brunswick repository. This repository houses the datasets used in this research and can be accessed publically in order to replicate the findings of this research.

  9. CICIDS2017

    • kaggle.com
    Updated May 23, 2024
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    Zufar Asyraf (2024). CICIDS2017 [Dataset]. https://www.kaggle.com/datasets/zufarasyraf/cicids2017/data
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    May 23, 2024
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Zufar Asyraf
    Description

    Dataset

    This dataset was created by Zufar Asyraf

    Contents

  10. P

    CICIDS2018 Dataset

    • paperswithcode.com
    Updated Aug 25, 2019
    + more versions
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    HUI ZHANG; Shenglong Zhou; Geoffrey Ye Li; Naihua Xiu (2019). CICIDS2018 Dataset [Dataset]. https://paperswithcode.com/dataset/cicids2018
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    Dataset updated
    Aug 25, 2019
    Authors
    HUI ZHANG; Shenglong Zhou; Geoffrey Ye Li; Naihua Xiu
    Description

    CICIDS2018 includes seven different attack scenarios: Brute-force, Heartbleed, Botnet, DoS, DDoS, Web attacks, and infiltration of the network from inside. The attacking infrastructure includes 50 machines and the victim organization has 5 departments and includes 420 machines and 30 servers. The dataset includes the captures network traffic and system logs of each machine, along with 80 features extracted from the captured traffic using CICFlowMeter-V3.

  11. f

    Literature review comprising main studies.

    • figshare.com
    xls
    Updated Jun 21, 2024
    + more versions
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    Nasrullah Khan; Muhammad Ismail Mohmand; Sadaqat ur Rehman; Zia Ullah; Zahid Khan; Wadii Boulila (2024). Literature review comprising main studies. [Dataset]. http://doi.org/10.1371/journal.pone.0299666.t001
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 21, 2024
    Dataset provided by
    PLOS ONE
    Authors
    Nasrullah Khan; Muhammad Ismail Mohmand; Sadaqat ur Rehman; Zia Ullah; Zahid Khan; Wadii Boulila
    License

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

    Description

    Computer networks face vulnerability to numerous attacks, which pose significant threats to our data security and the freedom of communication. This paper introduces a novel intrusion detection technique that diverges from traditional methods by leveraging Recurrent Neural Networks (RNNs) for both data preprocessing and feature extraction. The proposed process is based on the following steps: (1) training the data using RNNs, (2) extracting features from their hidden layers, and (3) applying various classification algorithms. This methodology offers significant advantages and greatly differs from existing intrusion detection practices. The effectiveness of our method is demonstrated through trials on the Network Security Laboratory (NSL) and Canadian Institute for Cybersecurity (CIC) 2017 datasets, where the application of RNNs for intrusion detection shows substantial practical implications. Specifically, we achieved accuracy scores of 99.6% with Decision Tree, Random Forest, and CatBoost classifiers on the NSL dataset, and 99.8% and 99.9%, respectively, on the CIC 2017 dataset. By reversing the conventional sequence of training data with RNNs and then extracting features before applying classification algorithms, our approach provides a major shift in intrusion detection methodologies. This modification in the pipeline underscores the benefits of utilizing RNNs for feature extraction and data preprocessing, meeting the critical need to safeguard data security and communication freedom against ever-evolving network threats.

  12. h

    CIC-IDS-2017

    • huggingface.co
    + more versions
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    Fikri Mulyana Setiawan, CIC-IDS-2017 [Dataset]. https://huggingface.co/datasets/fikrimulyana/CIC-IDS-2017
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    Authors
    Fikri Mulyana Setiawan
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Description

    fikrimulyana/CIC-IDS-2017 dataset hosted on Hugging Face and contributed by the HF Datasets community

  13. cicids2017

    • kaggle.com
    Updated Jun 18, 2025
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    Mohaned Mohammed Naji (2025). cicids2017 [Dataset]. https://www.kaggle.com/datasets/mohanedmohammednaji/cicids2017/code
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jun 18, 2025
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Mohaned Mohammed Naji
    Description

    Dataset

    This dataset was created by Mohaned Mohammed Naji

    Contents

  14. P

    CIC-DDoS2019 Dataset

    • paperswithcode.com
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    Roberto Doriguzzi-Corin; Stuart Millar; Sandra Scott-Hayward; Jesus Martinez-del-Rincon; Domenico Siracusa, CIC-DDoS2019 Dataset [Dataset]. https://paperswithcode.com/dataset/cic-ddos2019
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    Authors
    Roberto Doriguzzi-Corin; Stuart Millar; Sandra Scott-Hayward; Jesus Martinez-del-Rincon; Domenico Siracusa
    Description

    his is an academic intrusion detection dataset. All the credit goes to the original authors: Dr. Iman Sharafaldin, Dr. Saqib Hakak, Dr. Arash Habibi Lashkari Dr. Ali Ghorbani. Please cite their original paper.

    The dataset offers an extended set of Distributed Denial of Service attacks, most of which employ some form of amplification through reflection. The dataset shares its feature set with the other CIC NIDS datasets, IDS2017, IDS2018 and DoS2017

  15. h

    cic-ids-2017

    • huggingface.co
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    Son Nguyen, cic-ids-2017 [Dataset]. https://huggingface.co/datasets/sonnh-tech1/cic-ids-2017
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    Authors
    Son Nguyen
    License

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

    Description

    sonnh-tech1/cic-ids-2017 dataset hosted on Hugging Face and contributed by the HF Datasets community

  16. i

    Unified Multimodal Network Intrusion Detection Systems Dataset

    • ieee-dataport.org
    Updated Oct 19, 2024
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    Syed Wali Rizvi (2024). Unified Multimodal Network Intrusion Detection Systems Dataset [Dataset]. https://ieee-dataport.org/documents/unified-multimodal-network-intrusion-detection-systems-dataset
    Explore at:
    Dataset updated
    Oct 19, 2024
    Authors
    Syed Wali Rizvi
    License

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

    Description

    and contextual features

  17. f

    Features of the CIC-IDS 2017 network intrusion dataset.

    • plos.figshare.com
    xls
    Updated Apr 14, 2025
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    Sanchit Vashisht; Shalli Rani; Mohammad Shabaz (2025). Features of the CIC-IDS 2017 network intrusion dataset. [Dataset]. http://doi.org/10.1371/journal.pone.0321224.t002
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    xlsAvailable download formats
    Dataset updated
    Apr 14, 2025
    Dataset provided by
    PLOS ONE
    Authors
    Sanchit Vashisht; Shalli Rani; Mohammad Shabaz
    License

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

    Description

    Features of the CIC-IDS 2017 network intrusion dataset.

  18. h

    cic-ids-2017-textual

    • huggingface.co
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    Shiva Prasad Gyawali, cic-ids-2017-textual [Dataset]. https://huggingface.co/datasets/gyawalishiva/cic-ids-2017-textual
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    Authors
    Shiva Prasad Gyawali
    Description

    gyawalishiva/cic-ids-2017-textual dataset hosted on Hugging Face and contributed by the HF Datasets community

  19. CICIDS-2017 TUE

    • kaggle.com
    zip
    Updated May 14, 2020
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    Sweety (2020). CICIDS-2017 TUE [Dataset]. https://www.kaggle.com/sweety18/cicids2017-tue
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    zip(40046227 bytes)Available download formats
    Dataset updated
    May 14, 2020
    Authors
    Sweety
    Description

    Dataset

    This dataset was created by Sweety

    Contents

  20. f

    Datasets Repository

    • figshare.com
    html
    Updated Apr 20, 2024
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    Fabio Schuartz (2024). Datasets Repository [Dataset]. http://doi.org/10.6084/m9.figshare.25656966.v1
    Explore at:
    htmlAvailable download formats
    Dataset updated
    Apr 20, 2024
    Dataset provided by
    figshare
    Authors
    Fabio Schuartz
    License

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

    Description

    The NSL-KDD and CICIDS2017 datasets used on the research.

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xinpeng chen (2023). CICIDS2017 and UNBSW-NB15 [Dataset]. https://ieee-dataport.org/documents/cicids2017-and-unbsw-nb15

CICIDS2017 and UNBSW-NB15

Explore at:
Dataset updated
Dec 13, 2023
Authors
xinpeng chen
License

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

Description

DoS

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