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Data from: NF-ToN-IoT-v2
- researchdata.edu.au
Updated May 15, 2023+ more versions
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EmailClick to copy linkLink copiedCiteMr Mohanad Sarhan; Mr Mohanad Sarhan; Dr Siamak Layeghy; Dr Siamak Layeghy; Associate Professor Marius Portmann; Associate Professor Marius Portmann (2023). NF-ToN-IoT-v2 [Dataset]. http://doi.org/10.48610/38A2D07Unique identifierhttps://doi.org/10.48610/38A2D07Dataset updatedMay 15, 2023Dataset provided byThe University of QueenslandAuthorsMr Mohanad Sarhan; Mr Mohanad Sarhan; Dr Siamak Layeghy; Dr Siamak Layeghy; Associate Professor Marius Portmann; Associate Professor Marius PortmannLicensehttp://guides.library.uq.edu.au/deposit_your_data/terms_and_conditionshttp://guides.library.uq.edu.au/deposit_your_data/terms_and_conditions
DescriptionNetFlow Version 2 of the datasets is made up of 43 extended NetFlow features. The details of the datasets are published in: Mohanad Sarhan, Siamak Layeghy, and Marius Portmann, Towards a Standard Feature Set for Network Intrusion Detection System Datasets, Mobile Networks and Applications, 103, 108379, 2022 The use of the datasets for academic research purposes is granted in perpetuity after citing the above papers. For commercial purposes, it should be agreed upon by the authors. Please get in touch with the author Mohanad Sarhan for more details.
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NF-ToN-IoT-benign
- huggingface.co
Updated Jun 27, 2024+ more versionsShareFacebookTwitterEmailClick to copy linkLink copiedCiteNaveen Radhakrishnan (2024). NF-ToN-IoT-benign [Dataset]. https://huggingface.co/datasets/rnaveensrinivas/NF-ToN-IoT-benignCroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.Dataset updatedJun 27, 2024AuthorsNaveen RadhakrishnanLicenseMIT Licensehttps://opensource.org/licenses/MIT
License information was derived automaticallyDescriptionrnaveensrinivas/NF-ToN-IoT-benign dataset hosted on Hugging Face and contributed by the HF Datasets community
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NF-ToN-IoT
- researchdata.edu.au
Updated May 15, 2023+ more versionsShareFacebookTwitterEmailClick to copy linkLink copiedCiteMr Mohanad Sarhan; Mr Mohanad Sarhan; Dr Siamak Layeghy; Dr Siamak Layeghy; Associate Professor Marius Portmann; Associate Professor Marius Portmann (2023). NF-ToN-IoT [Dataset]. http://doi.org/10.48610/2FA2ED6Unique identifierhttps://doi.org/10.48610/2FA2ED6Dataset updatedMay 15, 2023Dataset provided byThe University of QueenslandAuthorsMr Mohanad Sarhan; Mr Mohanad Sarhan; Dr Siamak Layeghy; Dr Siamak Layeghy; Associate Professor Marius Portmann; Associate Professor Marius PortmannLicensehttp://guides.library.uq.edu.au/deposit_your_data/terms_and_conditionshttp://guides.library.uq.edu.au/deposit_your_data/terms_and_conditions
DescriptionNetFlow Version 1 of the datasets is made up of 8 basic NetFlow features. The details of the datasets are published in; Sarhan M., Layeghy S., Moustafa N., Portmann M. (2021) NetFlow Datasets for Machine Learning-Based Network Intrusion Detection Systems. In: Big Data Technologies and Applications. BDTA 2020, WiCON 2020. Springer, Cham. The use of the datasets for academic research purposes is granted in perpetuity after citing the above papers. For commercial purposes, it should be agreed upon by the authors. Please get in touch with the author Mohanad Sarhan for more details.
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CIC-ToN-IoT
- researchdata.edu.au
Updated May 15, 2023+ more versionsShareFacebookTwitterEmailClick to copy linkLink copiedCiteMr Mohanad Sarhan; Mr Mohanad Sarhan; Dr Siamak Layeghy; Dr Siamak Layeghy; Associate Professor Marius Portmann; Associate Professor Marius Portmann (2023). CIC-ToN-IoT [Dataset]. http://doi.org/10.48610/F6884CEUnique identifierhttps://doi.org/10.48610/F6884CEDataset updatedMay 15, 2023Dataset provided byThe University of QueenslandAuthorsMr Mohanad Sarhan; Mr Mohanad Sarhan; Dr Siamak Layeghy; Dr Siamak Layeghy; Associate Professor Marius Portmann; Associate Professor Marius PortmannLicensehttp://guides.library.uq.edu.au/deposit_your_data/terms_and_conditionshttp://guides.library.uq.edu.au/deposit_your_data/terms_and_conditions
DescriptionThe CICFlowMeter format of the datasets is made up of 83 network features. The details of the datasets are published in: Mohanad Sarhan, Siamak Layeghy, and Marius Portmann, Evaluating Standard Feature Sets Towards Increased Generalisability and Explainability of ML-based Network Intrusion Detection, Big Data Research, 30, 100359, 2022 The use of the datasets for academic research purposes is granted in perpetuity after citing the above papers. For commercial purposes, it should be agreed upon by the authors. Please get in touch with the author Mohanad Sarhan for more details.
Network traffic datasets created by Single Flow Time Series Analysis
- zenodo.org
- explore.openaire.eu
csv, pdfUpdated Jul 11, 2024ShareFacebookTwitterEmailClick to copy linkLink copiedCiteJosef Koumar; Josef Koumar; Karel Hynek; Karel Hynek; Tomáš Čejka; Tomáš Čejka (2024). Network traffic datasets created by Single Flow Time Series Analysis [Dataset]. http://doi.org/10.5281/zenodo.8035724csv, pdfAvailable download formatsUnique identifierhttps://doi.org/10.5281/zenodo.8035724Dataset updatedJul 11, 2024AuthorsJosef Koumar; Josef Koumar; Karel Hynek; Karel Hynek; Tomáš Čejka; Tomáš ČejkaLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescriptionNetwork traffic datasets created by Single Flow Time Series Analysis
Datasets were created for the paper: Network Traffic Classification based on Single Flow Time Series Analysis -- Josef Koumar, Karel Hynek, Tomáš Čejka -- which was published at The 19th International Conference on Network and Service Management (CNSM) 2023. Please cite usage of our datasets as:
J. Koumar, K. Hynek and T. Čejka, "Network Traffic Classification Based on Single Flow Time Series Analysis," 2023 19th International Conference on Network and Service Management (CNSM), Niagara Falls, ON, Canada, 2023, pp. 1-7, doi: 10.23919/CNSM59352.2023.10327876.
This Zenodo repository contains 23 datasets created from 15 well-known published datasets which are cited in the table below. Each dataset contains 69 features created by Time Series Analysis of Single Flow Time Series. The detailed description of features from datasets is in the file: feature_description.pdf
In the following table is a description of each dataset file:
File name Detection problem Citation of original raw dataset botnet_binary.csv Binary detection of botnet S. García et al. An Empirical Comparison of Botnet Detection Methods. Computers & Security, 45:100–123, 2014. botnet_multiclass.csv Multi-class classification of botnet S. García et al. An Empirical Comparison of Botnet Detection Methods. Computers & Security, 45:100–123, 2014. cryptomining_design.csv Binary detection of cryptomining; the design part Richard Plný et al. Datasets of Cryptomining Communication. Zenodo, October 2022 cryptomining_evaluation.csv Binary detection of cryptomining; the evaluation part Richard Plný et al. Datasets of Cryptomining Communication. Zenodo, October 2022 dns_malware.csv Binary detection of malware DNS Samaneh Mahdavifar et al. Classifying Malicious Domains using DNS Traffic Analysis. In DASC/PiCom/CBDCom/CyberSciTech 2021, pages 60–67. IEEE, 2021. doh_cic.csv Binary detection of DoH Mohammadreza MontazeriShatoori et al. Detection of doh tunnels using time-series classification of encrypted traffic. In DASC/PiCom/CBDCom/CyberSciTech 2020, pages 63–70. IEEE, 2020
doh_real_world.csv Binary detection of DoH Kamil Jeřábek et al. Collection of datasets with DNS over HTTPS traffic. Data in Brief, 42:108310, 2022 dos.csv Binary detection of DoS Nickolaos Koroniotis et al. Towards the development of realistic botnet dataset in the Internet of Things for network forensic analytics: Bot-IoT dataset. Future Gener. Comput. Syst., 100:779–796, 2019. edge_iiot_binary.csv Binary detection of IoT malware Mohamed Amine Ferrag et al. Edge-iiotset: A new comprehensive realistic cyber security dataset of iot and iiot applications: Centralized and federated learning, 2022. edge_iiot_multiclass.csv Multi-class classification of IoT malware Mohamed Amine Ferrag et al. Edge-iiotset: A new comprehensive realistic cyber security dataset of iot and iiot applications: Centralized and federated learning, 2022. https_brute_force.csv Binary detection of HTTPS Brute Force Jan Luxemburk et al. HTTPS Brute-force dataset with extended network flows, November 2020 ids_cic_binary.csv Binary detection of intrusion in IDS Iman Sharafaldin et al. Toward generating a new intrusion detection dataset and intrusion traffic characterization. ICISSp, 1:108–116, 2018. ids_cic_multiclass.csv Multi-class classification of intrusion in IDS Iman Sharafaldin et al. Toward generating a new intrusion detection dataset and intrusion traffic characterization. ICISSp, 1:108–116, 2018. ids_unsw_nb_15_binary.csv Binary detection of intrusion in IDS Nour Moustafa and Jill Slay. Unsw-nb15: a comprehensive data set for network intrusion detection systems (unsw-nb15 network data set). In 2015 military communications and information systems conference (MilCIS), pages 1–6. IEEE, 2015. ids_unsw_nb_15_multiclass.csv Multi-class classification of intrusion in IDS Nour Moustafa and Jill Slay. Unsw-nb15: a comprehensive data set for network intrusion detection systems (unsw-nb15 network data set). In 2015 military communications and information systems conference (MilCIS), pages 1–6. IEEE, 2015. iot_23.csv Binary detection of IoT malware Sebastian Garcia et al. IoT-23: A labeled dataset with malicious and benign IoT network traffic, January 2020. More details here https://www.stratosphereips.org /datasets-iot23 ton_iot_binary.csv Binary detection of IoT malware Nour Moustafa. A new distributed architecture for evaluating ai-based security systems at the edge: Network ton iot datasets. Sustainable Cities and Society, 72:102994, 2021 ton_iot_multiclass.csv Multi-class classification of IoT malware Nour Moustafa. A new distributed architecture for evaluating ai-based security systems at the edge: Network ton iot datasets. Sustainable Cities and Society, 72:102994, 2021 tor_binary.csv Binary detection of TOR Arash Habibi Lashkari et al. Characterization of Tor Traffic using Time based Features. In ICISSP 2017, pages 253–262. SciTePress, 2017. tor_multiclass.csv Multi-class classification of TOR Arash Habibi Lashkari et al. Characterization of Tor Traffic using Time based Features. In ICISSP 2017, pages 253–262. SciTePress, 2017. vpn_iscx_binary.csv Binary detection of VPN Gerard Draper-Gil et al. Characterization of Encrypted and VPN Traffic Using Time-related. In ICISSP, pages 407–414, 2016. vpn_iscx_multiclass.csv Multi-class classification of VPN Gerard Draper-Gil et al. Characterization of Encrypted and VPN Traffic Using Time-related. In ICISSP, pages 407–414, 2016. vpn_vnat_binary.csv Binary detection of VPN Steven Jorgensen et al. Extensible Machine Learning for Encrypted Network Traffic Application Labeling via Uncertainty Quantification. CoRR, abs/2205.05628, 2022 vpn_vnat_multiclass.csv Multi-class classification of VPN Steven Jorgensen et al. Extensible Machine Learning for Encrypted Network Traffic Application Labeling via Uncertainty Quantification. CoRR, abs/2205.05628, 2022 Data 2 Category
- figshare.com
txtUpdated Mar 23, 2022ShareFacebookTwitterEmailClick to copy linkLink copiedCiteZaid A Khan (2022). Data 2 Category [Dataset]. http://doi.org/10.6084/m9.figshare.19407242.v1txtAvailable download formatsUnique identifierhttps://doi.org/10.6084/m9.figshare.19407242.v1Dataset updatedMar 23, 2022AuthorsZaid A KhanLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescriptionToN IoT Category
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NetFlow Version 2 of the datasets is made up of 43 extended NetFlow features. The details of the datasets are published in: Mohanad Sarhan, Siamak Layeghy, and Marius Portmann, Towards a Standard Feature Set for Network Intrusion Detection System Datasets, Mobile Networks and Applications, 103, 108379, 2022 The use of the datasets for academic research purposes is granted in perpetuity after citing the above papers. For commercial purposes, it should be agreed upon by the authors. Please get in touch with the author Mohanad Sarhan for more details.