Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: ValueError
Message: Expected object or value
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 129, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 544, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 3052, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2559, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2584, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 558, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 440, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 281, in _generate_tables
examples = [ujson_loads(line) for line in batch.splitlines()]
~~~~~~~~~~~^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 20, in ujson_loads
return pd.io.json.ujson_loads(*args, **kwargs)
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
ValueError: Expected object or valueNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
MorphGuard log datasets
Raw and derived data behind MorphGuard (Large-Scale Log Analytics for Cloud
Security). Code: https://github.com/Ashwin-deals/large-scale-log-analytics-backend.
Fetch everything with python3 scripts/download_dataset.py --source all --with-derived.
Layout
| Path | What it is | Labels |
|---|---|---|
HDFS.log, anomaly_label.csv |
LogHub HDFS_v1: 11,175,629 lines, 575,061 blocks | Ground truth per block (16,838 anomalous) |
openstack/ |
LogHub OpenStack: openstack_normal1.log, openstack_normal2.log, openstack_abnormal.log, anomaly_labels.txt |
By run: a session from the abnormal run is labeled Anomaly. Only 4 instances are documented as faulty. |
apache/Apache.log |
LogHub Apache error log, 56,481 lines | None |
apache/injected/ |
Apache_INJECTED.log and _labels.csv, from scripts/inject_apache_anomalies.py (seed 42) |
Injected anomalies |
cloudtrail/ |
flAWS.cloud CloudTrail logs, flaws_cloudtrail00-19.json.gz, 1,939,207 events |
None. MorphGuard derives weak rule-based labels. |
django/ |
django_SYNTHETIC.log and _labels.csv, from scripts/generate_django_logs.py (seed 42) |
Synthetic |
derived/hdfs/ |
features.csv, block_metadata.csv, dataset_stats.json built from the HDFS log |
Not applicable |
derived/vocab/<source>/ |
category_encoders.json: the category vocabulary every model is stamped with. Scoring refuses to run without it. |
Not applicable |
What the labels can and cannot support
- HDFS: validated ground truth.
- OpenStack: a labeled case study. The labels mark which run a session came from, not which session failed.
- Apache, CloudTrail, Django: pipeline tests with injected, rule-based or synthetic labels. F1 on these measures agreement with those labels, not with real incidents.
Sources and credit
- HDFS, OpenStack and Apache: LogHub, https://github.com/logpai/loghub (Zenodo record 8196385). Please cite the LogHub paper (Zhu et al., Loghub: A Large Collection of System Log Datasets for AI-driven Log Analytics, ISSRE 2023). The OpenStack logs originate from the DeepLog paper (Du et al., CCS 2017).
- CloudTrail: the flAWS.cloud logs published by Scott Piper / Summit Route, http://flaws.cloud/.
- Injected Apache and synthetic Django files: generated by MorphGuard's scripts.
Before publishing this repo: check each third-party source's licence or terms for re-hosting.
If a source does not allow it, remove its folder here. In that case, point scripts/download_dataset.py at the original link for that source.
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