The dataset viewer is not available for this dataset.
Error code: ConfigNamesError
Exception: ReadTimeout
Message: (ReadTimeoutError("HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)"), '(Request ID: 96fbebb9-92fe-49f1-90f4-30b1606466eb)')
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 164, in get_dataset_config_names
dataset_module = dataset_module_factory(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1729, in dataset_module_factory
raise e1 from None
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1686, in dataset_module_factory
return HubDatasetModuleFactoryWithoutScript(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1065, in get_module
data_files = DataFilesDict.from_patterns(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/data_files.py", line 721, in from_patterns
else DataFilesList.from_patterns(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/data_files.py", line 634, in from_patterns
origin_metadata = _get_origin_metadata(data_files, download_config=download_config)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/data_files.py", line 548, in _get_origin_metadata
return thread_map(
File "/src/services/worker/.venv/lib/python3.9/site-packages/tqdm/contrib/concurrent.py", line 69, in thread_map
return _executor_map(ThreadPoolExecutor, fn, *iterables, **tqdm_kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/tqdm/contrib/concurrent.py", line 51, in _executor_map
return list(tqdm_class(ex.map(fn, *iterables, chunksize=chunksize), **kwargs))
File "/src/services/worker/.venv/lib/python3.9/site-packages/tqdm/std.py", line 1169, in __iter__
for obj in iterable:
File "/usr/local/lib/python3.9/concurrent/futures/_base.py", line 609, in result_iterator
yield fs.pop().result()
File "/usr/local/lib/python3.9/concurrent/futures/_base.py", line 446, in result
return self.__get_result()
File "/usr/local/lib/python3.9/concurrent/futures/_base.py", line 391, in __get_result
raise self._exception
File "/usr/local/lib/python3.9/concurrent/futures/thread.py", line 58, in run
result = self.fn(*self.args, **self.kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/data_files.py", line 527, in _get_single_origin_metadata
resolved_path = fs.resolve_path(data_file)
File "/src/services/worker/.venv/lib/python3.9/site-packages/huggingface_hub/hf_file_system.py", line 175, in resolve_path
repo_and_revision_exist, err = self._repo_and_revision_exist(repo_type, repo_id, revision)
File "/src/services/worker/.venv/lib/python3.9/site-packages/huggingface_hub/hf_file_system.py", line 121, in _repo_and_revision_exist
self._api.repo_info(
File "/src/services/worker/.venv/lib/python3.9/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
return fn(*args, **kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/huggingface_hub/hf_api.py", line 2682, in repo_info
return method(
File "/src/services/worker/.venv/lib/python3.9/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
return fn(*args, **kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/huggingface_hub/hf_api.py", line 2539, in dataset_info
r = get_session().get(path, headers=headers, timeout=timeout, params=params)
File "/src/services/worker/.venv/lib/python3.9/site-packages/requests/sessions.py", line 602, in get
return self.request("GET", url, **kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/requests/sessions.py", line 589, in request
resp = self.send(prep, **send_kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/requests/sessions.py", line 703, in send
r = adapter.send(request, **kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/huggingface_hub/utils/_http.py", line 93, in send
return super().send(request, *args, **kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/requests/adapters.py", line 635, in send
raise ReadTimeout(e, request=request)
requests.exceptions.ReadTimeout: (ReadTimeoutError("HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)"), '(Request ID: 96fbebb9-92fe-49f1-90f4-30b1606466eb)')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.
The NSL-KDD V2 is an extended version of NSL-KDD original dataset. The dataset is normalised and 1 additional class is synthesised by mixing 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 https://www.researchgate.net/publication/382034618_Blender-GAN_Multi-Target_Conditional_Generative_Adversarial_Network_for_Novel_Class_Synthetic_Data_Generation.
Citation info:
Madhubalan, Akshayraj & Gautam, Amit & Tiwary, Priya. (2024). Blender-GAN: Multi-Target Conditional Generative Adversarial Network for Novel Class Synthetic Data Generation. 1-7. 10.1109/SmartNets61466.2024.10577645.
This dataset was made by Abluva Inc, a Palo Alto based, research-driven Data Protection firm. Our data protection platform empowers customers to secure data through advanced security mechanisms such as Fine Grained Access control and sophisticated depersonalization algorithms (e.g. Pseudonymization, Anonymization and Randomization). Abluva's Data Protection solutions facilitate data democratization within and outside the organizations, mitigating the concerns related to theft and compliance. The innovative intrusion detection algorithm by Abluva employs patented technologies for an intricately balanced approach that excludes normal access deviations, ensuring intrusion detection without disrupting the business operations. Abluva’s Solution enables organizations to extract further value from their data by enabling secure Knowledge Graphs and deploying Secure Data as a Service among other novel uses of data. Committed to providing a safe and secure environment, Abluva empowers organizations to unlock the full potential of their data.
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