datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
UNSW-NB15We have developed a Python package as a wrapper around Hugging Face Hub and Hugging Face Datasets library to access this dataset easily.
NIDS Datasets
The nids-datasets package provides functionality to download and utilize specially curated and extracted datasets from the original UNSW-NB15 and CIC-IDS2017 datasets. These datasets, which initially were only flow datasets, have been enhanced to include packet-level information from the raw PCAP files. The dataset contains both… See the full description on the dataset page: https://huggingface.co/datasets/rdpahalavan/UNSW-NB15.UNSW-NB15
The UNSW-NB15
The raw network packets (Pcap files) of the UNSW-NB 15 data set is created by the IXIA
PerfectStorm tool in the Cyber Range Lab of the Australian Centre for Cyber Security (ACCS)
for generating a hybrid of real modern normal activities and synthetic contemporary attack
activities. The UNSW-NB15 source files are provided in different formats, Pcap files, BRO files,
Argus Files and CSV files. The source files of the data set were divided based in the date of the… See the full description on the dataset page: https://huggingface.co/datasets/Mouwiya/UNSW-NB15.UNSW-NB15
UNSW-NB15 Network Intrusion Detection Dataset
The UNSW-NB15 dataset for network intrusion detection, provided with two evaluation protocols to enable fair comparison across the literature.
Why This Dataset Exists
Published results on UNSW-NB15 range from 85% to 99% accuracy — but the gap is almost entirely due to evaluation protocol differences, not model quality:
Evaluation Protocol
Typical Accuracy
Example Papers
Standard split (temporal, 175K/82K)
85–93%… See the full description on the dataset page: https://huggingface.co/datasets/lacg030175/UNSW-NB15.UNSW-NB15
UNSW-NB15
This data is provided through the Train, Test CSV file provided by UNSW-NB15.
link
Labels
The label of the data set is as follows.
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Dtype
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id
82332
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state
82332
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spkts
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sbytes
82332
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dbytes
82332
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rate… See the full description on the dataset page: https://huggingface.co/datasets/Mireu-Lab/UNSW-NB15.UNSW-NB15-smallBESSTIE
BESSTIE: A Benchmark for Sentiment and Sarcasm Classification for Varieties of English
Authors: Dipankar Srirag, Aditya Joshi, Jordan Painter, and Diptesh Kanojia.
In Findings of the Association for Computational Linguistics: ACL 2025, Vienna, Austria.
Abstract
Despite large language models (LLMs) being known to exhibit bias against non-mainstream varieties, there are no known labeled datasets for sentiment analysis of English. To address this gap, we… See the full description on the dataset page: https://huggingface.co/datasets/unswnlporg/BESSTIE.new_dataset_unswappedunsw-nb15CIC-UNSW-NB15UNSW-NB15
UNSW-NB15
This data is provided through the Train, Test CSV file provided by UNSW-NB15.
link
Labels
The label of the data set is as follows.
#
Column
Non-Null
Count
Dtype
0
id
82332
non-null
int64
1
dur
82332
non-null
float64
2
proto
82332
non-null
object
3
service
82332
non-null
object
4
state
82332
non-null
object
5
spkts
82332
non-null
int64
6
dpkts
82332
non-null
int64
7
sbytes
82332
non-null
int64
8
dbytes
82332
non-null
int64
9
rate… See the full description on the dataset page: https://huggingface.co/datasets/jharrrry/UNSW-NB15.bearing-run2failure-unswThis dataset is a subset of the bearing run-to-failure dataset of UNSW :
Horizontal acceleration (accH) measurements
Test 1 and Test 3 from the original dataset
Sampling frequency: 51200 Hz
Each sample contains signal data, signal length, sampling frequency, speed (6.0 Hz), radial load (10.5 kN), defect size (1.0 and 0.5 mm), and defect type (BPFO)
Implementation
This dataset is used as the validation dataset in this RUL prediction project:… See the full description on the dataset page: https://huggingface.co/datasets/alidi/bearing-run2failure-unsw.continual-unsw-anomaly-detectionUNSW-IoT
Dataset Card for Dataset Name
Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
Supported Tasks and Leaderboards
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Languages
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Dataset Structure
Data Instances
[More Information Needed]
Data Fields
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Data Splits
[More Information Needed]
Dataset Creation… See the full description on the dataset page: https://huggingface.co/datasets/Mireu-Lab/UNSW-IoT.UNSW_json_testunsw-nb15-preprocessed
Dataset Card for Dataset Name
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
Dataset Details
Dataset Description
Curated by: [More Information Needed]
Funded by [optional]: [More Information Needed]
Shared by [optional]: [More Information Needed]
Language(s) (NLP): [More Information Needed]
License: [More Information Needed]
Dataset Sources [optional]
Repository: [More… See the full description on the dataset page: https://huggingface.co/datasets/louiecerv/unsw-nb15-preprocessed.unsw-nb15-parquetUNSW-NB15-V3The dataset is an extended version of UNSW-NB 15. It has 1 additional class synthesised and the data is normalised for ease of use.
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: https://www.researchgate.net/publication/382034618_Blender-GAN_Multi-Target_Conditional_Generative_Adversarial_Network_for_Novel_Class_Synthetic_Data_Generation.
Citation info:
Madhubalan… See the full description on the dataset page: https://huggingface.co/datasets/abluva/UNSW-NB15-V3.TestTemplateLLM-UNSW-NB15unswUNSW_train_0408UNSW_train_0409