The dataset viewer is not available for this dataset.
Error code: ConfigNamesError
Exception: ImportError
Message: To be able to use codymlewis/nbaiot, you need to install the following dependency: rarfile.
Please install it using 'pip install rarfile' for instance.
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 66, in compute_config_names_response
config_names = get_dataset_config_names(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 347, in get_dataset_config_names
dataset_module = dataset_module_factory(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1914, in dataset_module_factory
raise e1 from None
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1880, in dataset_module_factory
return HubDatasetModuleFactoryWithScript(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1504, in get_module
local_imports = _download_additional_modules(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 354, in _download_additional_modules
raise ImportError(
ImportError: To be able to use codymlewis/nbaiot, you need to install the following dependency: rarfile.
Please install it using 'pip install rarfile' for instance.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Dataset Card for N-BAIoT
From https://archive.ics.uci.edu/dataset/442/detection+of+iot+botnet+attacks+n+baiot: This dataset addresses the lack of public botnet datasets, especially for the IoT. It suggests real traffic data, gathered from 9 commercial IoT devices authentically infected by Mirai and BASHLITE.
Dataset Details
Dataset Description
From https://archive.ics.uci.edu/dataset/442/detection+of+iot+botnet+attacks+n+baiot: (a) Attribute being predicted: -- Originally we aimed at distinguishing between benign and Malicious traffic data by means of anomaly detection techniques. -- However, as the malicious data can be divided into 10 attacks carried by 2 botnets, the dataset can also be used for multi-class classification: 10 classes of attacks, plus 1 class of 'benign'.
(b) The study's results: -- For each of the 9 IoT devices we trained and optimized a deep autoencoder on 2/3 of its benign data (i.e., the training set of each device). This was done to capture normal network traffic patterns. -- The test data of each device comprised of the remaining 1/3 of benign data plus all the malicious data. On each test set we applied the respective trained (deep) autoencoder as an anomaly detector. The detection of anomalies (i.e., the cyberattacks launched from each of the above IoT devices) concluded with 100% TPR.
- Curated by: Meidan, Yair, Bohadana, Michael, Mathov, Yael, Mirsky, Yisroel, Breitenbacher, Dominik, , Asaf, and Shabtai, Asaf
- License: Creative Commons Attribution 4.0 International (CC BY 4.0)
Dataset Sources
- Repository: https://archive.ics.uci.edu/dataset/442/detection+of+iot+botnet+attacks+n+baiot
- Paper: https://arxiv.org/abs/1805.03409
Citation
BibTeX:
@misc{misc_detection_of_iot_botnet_attacks_n_baiot_442, author = {Meidan,Yair, Bohadana,Michael, Mathov,Yael, Mirsky,Yisroel, Breitenbacher,Dominik, ,Asaf, and Shabtai,Asaf}, title = {{N-BaIoT Dataset to Detect IoT Botnet Attacks}}, year = {2018}, howpublished = {UCI Machine Learning Repository}, note = {{DOI}: https://doi.org/10.24432/C5RC8J} }
APA:
Meidan, Yair, Bohadana, Michael, Mathov, Yael, Mirsky, Yisroel, Breitenbacher, Dominik, ,Asaf, and Shabtai, Asaf. (2018). N-BaIoT Dataset to Detect IoT Botnet Attacks. UCI Machine Learning Repository. https://doi.org/10.24432/C5RC8J.
Glossary [optional]
- IoT: Internet of Things
- Botnet: A collection of devices that are maliciously controlled via malware
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