{"url":"/dataset/unsw-nb15","name":"UNSW-NB15","full_name":"UNSQ-NB15","description_markdown":"**UNSW-NB15** is a network intrusion dataset. It contains nine different attacks, includes DoS, worms, Backdoors, and Fuzzers. The dataset contains raw network packets. The number of records in the training set is 175,341 records and the testing set is 82,332 records from the different types, attack and normal.\r\n\r\nSource: [Evaluation of Adversarial Training on Different Types of Neural Networks in Deep Learning-based IDSs](https://arxiv.org/abs/2007.04472)\r\nImage Source: [https://www.unsw.adfa.edu.au/unsw-canberra-cyber/cybersecurity/ADFA-NB15-Datasets/](https://www.unsw.adfa.edu.au/unsw-canberra-cyber/cybersecurity/ADFA-NB15-Datasets/)\r\nPaper: [UNSW-NB15: a comprehensive data set for network intrusion detection systems](https://doi.org/10.1109/MilCIS.2015.7348942)","description_withheld":null,"homepage":"https://www.unsw.adfa.edu.au/unsw-canberra-cyber/cybersecurity/ADFA-NB15-Datasets/","introduced_date":"2015-11-01","introduced_date_note":null,"introduced_by":{"paper":null,"title":"UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set)","first_author":null,"url":"https://doi.org/10.1109/MilCIS.2015.7348942"},"license":{"name":"Custom (research)","url":"https://www.unsw.adfa.edu.au/unsw-canberra-cyber/cybersecurity/ADFA-NB15-Datasets/"},"modalities":[{"name":"Tabular","url":"/datasets/modality/tabular"}],"tasks":[{"name":"Intrusion Detection","url":"/task/intrusion-detection","datasets_with_task":"/datasets/task/intrusion-detection"},{"name":"Network Intrusion Detection","url":"/task/network-intrusion-detection","datasets_with_task":"/datasets/task/network-intrusion-detection"},{"name":"Synthetic Data Generation","url":"/task/synthetic-data-generation","datasets_with_task":"/datasets/task/synthetic-data-generation"}],"languages":[{"name":"Russian","url":"/datasets/language/russian"}],"variants":["UNSW-NB15"],"data_loaders":[{"repo":"https://github.com/Kaggle/kaggle-api","url":"https://www.kaggle.com/datasets/dhoogla/unswnb15","frameworks":[]}],"num_papers_in_archive":156,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/network-intrusion-detection-on-unsw-nb15","task":"Network Intrusion Detection","dataset_variant":"UNSW-NB15","rows":2,"metrics":["Accuracy","Precision","Recall"],"first_row_in_archive_order":{"model":"Edge-Detect-FRNN","paper":"/paper/edge-detect-edge-centric-network-intrusion","metrics":{"Accuracy":"99.6","Precision":"99.5","Recall":"99.75"},"code_links":[{"title":"racsa-lab/EDD","url":"https://github.com/racsa-lab/EDD"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/synthetic-data-generation-on-unsw-nb15","task":"Synthetic Data Generation","dataset_variant":"UNSW-NB15","rows":2,"metrics":["EMD"],"first_row_in_archive_order":{"model":"kiNETGAN","paper":"/paper/kinetgan-enabling-distributed-network","metrics":{"EMD":"0.07"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/intrusion-detection-on-unsw-nb15","task":"Intrusion Detection","dataset_variant":"UNSW-NB15","rows":1,"metrics":["AUC"],"first_row_in_archive_order":{"model":"MSTREAM-AE","paper":"/paper/mstream-fast-streaming-multi-aspect-group","metrics":{"AUC":"0.90"},"code_links":[{"title":"Stream-AD/MStream","url":"https://github.com/Stream-AD/MStream"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/kinetgan-enabling-distributed-network","title":"KiNETGAN: Enabling Distributed Network Intrusion Detection through Knowledge-Infused Synthetic Data Generation","date":"2024-05-26","rows_on_this_dataset":2,"code_links":0,"syntology":null},{"paper":"/paper/edge-detect-edge-centric-network-intrusion","title":"Edge-Detect: Edge-centric Network Intrusion Detection using Deep Neural Network","date":"2021-02-03","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/mstream-fast-streaming-multi-aspect-group","title":"MSTREAM: Fast Anomaly Detection in Multi-Aspect Streams","date":"2020-09-17","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}