Datasets:

anonymizeddb
/

CAD-CICIDS2017

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
source: string
task_id_column: string
orderings: list<item: struct<name: string, task_sequence: list<item: string>>>
  child 0, item: struct<name: string, task_sequence: list<item: string>>
      child 0, name: string
      child 1, task_sequence: list<item: string>
          child 0, item: string
rai:hasSyntheticData: bool
rai:dataBiases: string
distribution: list<item: struct<@type: string, @id: string, name: string, description: string, contentSize: string (... 62 chars omitted)
  child 0, item: struct<@type: string, @id: string, name: string, description: string, contentSize: string, contentUr (... 50 chars omitted)
      child 0, @type: string
      child 1, @id: string
      child 2, name: string
      child 3, description: string
      child 4, contentSize: string
      child 5, contentUrl: string
      child 6, encodingFormat: string
      child 7, sha256: string
license: string
description: string
creator: struct<@type: string, name: string>
  child 0, @type: string
  child 1, name: string
rai:dataSocialImpact: string
url: string
@type: string
name: string
rai:personalSensitiveInformation: string
datePublished: timestamp[s]
rai:dataUseCases: list<item: string>
  child 0, item: string
prov:wasGeneratedBy: string
version: string
prov:wasDerivedFrom: string
inLanguage: string
recordSet: list<item: struct<@type: string, @id: string, name: string, description: string, field: list<item: s (... 335 chars omitted)
  child 0, item: struct<@type: string, @id: string, name: str
...
rds: list<item: string>
  child 0, item: string
@context: struct<@language: string, @vocab: string, citeAs: string, column: string, conformsTo: string, cr: st (... 615 chars omitted)
  child 0, @language: string
  child 1, @vocab: string
  child 2, citeAs: string
  child 3, column: string
  child 4, conformsTo: string
  child 5, cr: string
  child 6, rai: string
  child 7, data: struct<@id: string, @type: string>
      child 0, @id: string
      child 1, @type: string
  child 8, dataType: struct<@id: string, @type: string>
      child 0, @id: string
      child 1, @type: string
  child 9, dct: string
  child 10, equivalentProperty: string
  child 11, examples: struct<@id: string, @type: string>
      child 0, @id: string
      child 1, @type: string
  child 12, extract: string
  child 13, field: string
  child 14, fileProperty: string
  child 15, fileObject: string
  child 16, fileSet: string
  child 17, format: string
  child 18, includes: string
  child 19, isLiveDataset: string
  child 20, jsonPath: string
  child 21, key: string
  child 22, md5: string
  child 23, parentField: string
  child 24, path: string
  child 25, recordSet: string
  child 26, references: string
  child 27, regex: string
  child 28, repeated: string
  child 29, replace: string
  child 30, samplingRate: string
  child 31, sc: string
  child 32, separator: string
  child 33, source: string
  child 34, subField: string
  child 35, transform: string
conformsTo: list<item: string>
  child 0, item: string
to
{'@context': {'@language': Value('string'), '@vocab': Value('string'), 'citeAs': Value('string'), 'column': Value('string'), 'conformsTo': Value('string'), 'cr': Value('string'), 'rai': Value('string'), 'data': {'@id': Value('string'), '@type': Value('string')}, 'dataType': {'@id': Value('string'), '@type': Value('string')}, 'dct': Value('string'), 'equivalentProperty': Value('string'), 'examples': {'@id': Value('string'), '@type': Value('string')}, 'extract': Value('string'), 'field': Value('string'), 'fileProperty': Value('string'), 'fileObject': Value('string'), 'fileSet': Value('string'), 'format': Value('string'), 'includes': Value('string'), 'isLiveDataset': Value('string'), 'jsonPath': Value('string'), 'key': Value('string'), 'md5': Value('string'), 'parentField': Value('string'), 'path': Value('string'), 'recordSet': Value('string'), 'references': Value('string'), 'regex': Value('string'), 'repeated': Value('string'), 'replace': Value('string'), 'samplingRate': Value('string'), 'sc': Value('string'), 'separator': Value('string'), 'source': Value('string'), 'subField': Value('string'), 'transform': Value('string')}, '@type': Value('string'), 'name': Value('string'), 'description': Value('string'), 'conformsTo': List(Value('string')), 'citeAs': Value('string'), 'creator': {'@type': Value('string'), 'name': Value('string')}, 'inLanguage': Value('string'), 'keywords': List(Value('string')), 'license': Value('string'), 'prov:wasDerivedFrom': Value('string'), 'prov:wasGeneratedBy': Value('string'), 'rai:dataBiases': Value('string'), 'rai:dataLimitations': Value('string'), 'rai:dataSocialImpact': Value('string'), 'rai:dataUseCases': List(Value('string')), 'rai:hasSyntheticData': Value('bool'), 'rai:personalSensitiveInformation': Value('string'), 'url': Value('string'), 'version': Value('string'), 'distribution': List({'@type': Value('string'), '@id': Value('string'), 'name': Value('string'), 'description': Value('string'), 'contentSize': Value('string'), 'contentUrl': Value('string'), 'encodingFormat': Value('string'), 'sha256': Value('string')}), 'recordSet': List({'@type': Value('string'), '@id': Value('string'), 'name': Value('string'), 'description': Value('string'), 'field': List({'@type': Value('string'), '@id': Value('string'), 'name': Value('string'), 'dataType': Value('string'), 'source': {'fileObject': {'@id': Value('string')}, 'extract': {'column': Value('string')}}, 'references': {'field': {'@id': Value('string')}}, 'repeated': Value('bool')}), 'key': {'@id': Value('string')}, 'data': List({'orderings/name': Value('string'), 'orderings/task_sequence': List(Value('string'))})}), 'datePublished': Value('timestamp[s]')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                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 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2227, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2251, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 494, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 384, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 299, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 128, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2321, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2249, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              source: string
              task_id_column: string
              orderings: list<item: struct<name: string, task_sequence: list<item: string>>>
                child 0, item: struct<name: string, task_sequence: list<item: string>>
                    child 0, name: string
                    child 1, task_sequence: list<item: string>
                        child 0, item: string
              rai:hasSyntheticData: bool
              rai:dataBiases: string
              distribution: list<item: struct<@type: string, @id: string, name: string, description: string, contentSize: string (... 62 chars omitted)
                child 0, item: struct<@type: string, @id: string, name: string, description: string, contentSize: string, contentUr (... 50 chars omitted)
                    child 0, @type: string
                    child 1, @id: string
                    child 2, name: string
                    child 3, description: string
                    child 4, contentSize: string
                    child 5, contentUrl: string
                    child 6, encodingFormat: string
                    child 7, sha256: string
              license: string
              description: string
              creator: struct<@type: string, name: string>
                child 0, @type: string
                child 1, name: string
              rai:dataSocialImpact: string
              url: string
              @type: string
              name: string
              rai:personalSensitiveInformation: string
              datePublished: timestamp[s]
              rai:dataUseCases: list<item: string>
                child 0, item: string
              prov:wasGeneratedBy: string
              version: string
              prov:wasDerivedFrom: string
              inLanguage: string
              recordSet: list<item: struct<@type: string, @id: string, name: string, description: string, field: list<item: s (... 335 chars omitted)
                child 0, item: struct<@type: string, @id: string, name: str
              ...
              rds: list<item: string>
                child 0, item: string
              @context: struct<@language: string, @vocab: string, citeAs: string, column: string, conformsTo: string, cr: st (... 615 chars omitted)
                child 0, @language: string
                child 1, @vocab: string
                child 2, citeAs: string
                child 3, column: string
                child 4, conformsTo: string
                child 5, cr: string
                child 6, rai: string
                child 7, data: struct<@id: string, @type: string>
                    child 0, @id: string
                    child 1, @type: string
                child 8, dataType: struct<@id: string, @type: string>
                    child 0, @id: string
                    child 1, @type: string
                child 9, dct: string
                child 10, equivalentProperty: string
                child 11, examples: struct<@id: string, @type: string>
                    child 0, @id: string
                    child 1, @type: string
                child 12, extract: string
                child 13, field: string
                child 14, fileProperty: string
                child 15, fileObject: string
                child 16, fileSet: string
                child 17, format: string
                child 18, includes: string
                child 19, isLiveDataset: string
                child 20, jsonPath: string
                child 21, key: string
                child 22, md5: string
                child 23, parentField: string
                child 24, path: string
                child 25, recordSet: string
                child 26, references: string
                child 27, regex: string
                child 28, repeated: string
                child 29, replace: string
                child 30, samplingRate: string
                child 31, sc: string
                child 32, separator: string
                child 33, source: string
                child 34, subField: string
                child 35, transform: string
              conformsTo: list<item: string>
                child 0, item: string
              to
              {'@context': {'@language': Value('string'), '@vocab': Value('string'), 'citeAs': Value('string'), 'column': Value('string'), 'conformsTo': Value('string'), 'cr': Value('string'), 'rai': Value('string'), 'data': {'@id': Value('string'), '@type': Value('string')}, 'dataType': {'@id': Value('string'), '@type': Value('string')}, 'dct': Value('string'), 'equivalentProperty': Value('string'), 'examples': {'@id': Value('string'), '@type': Value('string')}, 'extract': Value('string'), 'field': Value('string'), 'fileProperty': Value('string'), 'fileObject': Value('string'), 'fileSet': Value('string'), 'format': Value('string'), 'includes': Value('string'), 'isLiveDataset': Value('string'), 'jsonPath': Value('string'), 'key': Value('string'), 'md5': Value('string'), 'parentField': Value('string'), 'path': Value('string'), 'recordSet': Value('string'), 'references': Value('string'), 'regex': Value('string'), 'repeated': Value('string'), 'replace': Value('string'), 'samplingRate': Value('string'), 'sc': Value('string'), 'separator': Value('string'), 'source': Value('string'), 'subField': Value('string'), 'transform': Value('string')}, '@type': Value('string'), 'name': Value('string'), 'description': Value('string'), 'conformsTo': List(Value('string')), 'citeAs': Value('string'), 'creator': {'@type': Value('string'), 'name': Value('string')}, 'inLanguage': Value('string'), 'keywords': List(Value('string')), 'license': Value('string'), 'prov:wasDerivedFrom': Value('string'), 'prov:wasGeneratedBy': Value('string'), 'rai:dataBiases': Value('string'), 'rai:dataLimitations': Value('string'), 'rai:dataSocialImpact': Value('string'), 'rai:dataUseCases': List(Value('string')), 'rai:hasSyntheticData': Value('bool'), 'rai:personalSensitiveInformation': Value('string'), 'url': Value('string'), 'version': Value('string'), 'distribution': List({'@type': Value('string'), '@id': Value('string'), 'name': Value('string'), 'description': Value('string'), 'contentSize': Value('string'), 'contentUrl': Value('string'), 'encodingFormat': Value('string'), 'sha256': Value('string')}), 'recordSet': List({'@type': Value('string'), '@id': Value('string'), 'name': Value('string'), 'description': Value('string'), 'field': List({'@type': Value('string'), '@id': Value('string'), 'name': Value('string'), 'dataType': Value('string'), 'source': {'fileObject': {'@id': Value('string')}, 'extract': {'column': Value('string')}}, 'references': {'field': {'@id': Value('string')}}, 'repeated': Value('bool')}), 'key': {'@id': Value('string')}, 'data': List({'orderings/name': Value('string'), 'orderings/task_sequence': List(Value('string'))})}), 'datePublished': Value('timestamp[s]')}
              because column names don't match

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CAD-CICIDS2017

Dataset Summary

CAD-CICIDS2017 is a single-source continual anomaly detection benchmark scenario for network intrusion detection. It is derived from CIC-IDS2017 and converts the original tabular network-intrusion data into a sequence of concept-grouped tasks.

The dataset contains 2,076,848 samples, 6 tasks, and has a reported 18.77% anomaly ratio in the test set.

The dataset is anonymized for double-blind NeurIPS review. Author names, institutional affiliations, project acknowledgements, and non-anonymous paper references are intentionally omitted.

Intended Use

This dataset is intended for research on:

continual anomaly detection;
continual learning for tabular data;
network intrusion detection;
robustness under distribution shift;
task ordering in continual-learning benchmarks;
forgetting and knowledge transfer across related network-traffic concepts;
benchmarking anomaly detectors under sequential task exposure.

The intended use is defensive machine learning research. The dataset should not be used to support offensive cybersecurity activity.

Dataset Source

CIC-IDS2017: https://www.unb.ca/cic/datasets/ids-2017.html

Dataset Files

The repository contains the following files:

File	Description
`data.csv`	Main tabular dataset file.
`orderings.json`	Predefined task orderings for continual-learning evaluation.
`croissant.json`	Croissant metadata describing the dataset.

Dataset Structure

The main file is:

data.csv

The dataset contains task metadata, binary labels, and numerical flow-level features.

Core Columns

Column	Type	Description
`task_id`	integer	Numeric identifier of the continual-learning task.
`task_name`	string	Name of the task, e.g. `cicids2017_0`.
`task_split`	string	Split assignment for the row.
`label`	integer	Binary anomaly label. `0` denotes benign/normal traffic and `1` denotes anomalous/attack traffic.

Task Identifiers

The dataset contains the following task identifiers:

cicids2017_0, cicids2017_1, cicids2017_2, cicids2017_3, cicids2017_4, cicids2017_5

Feature Columns

The remaining columns are numerical network-flow features, including packet-count, byte-count, flag-count, duration, inter-arrival-time, and aggregate flow-statistics features. Representative examples include:

Destination Port
Flow Duration
Flow Bytes/s
Flow Packets/s
Total Fwd Packets
Total Backward Packets
Total Length of Fwd Packets
Total Length of Bwd Packets
Fwd Packet Length Mean
Bwd Packet Length Mean
Flow IAT Mean
Fwd IAT Mean
Bwd IAT Mean

For the complete schema, see croissant.json.

Task Orderings

The dataset provides six predefined orderings in orderings.json. These orderings define different continual-learning evaluation regimes over the same task set.

Ordering	Task sequence
`curriculum_asc`	`cicids2017_5` → `cicids2017_2` → `cicids2017_0` → `cicids2017_3` → `cicids2017_4` → `cicids2017_1`
`curriculum_desc`	`cicids2017_1` → `cicids2017_4` → `cicids2017_3` → `cicids2017_0` → `cicids2017_2` → `cicids2017_5`
`generalization_desc`	`cicids2017_4` → `cicids2017_3` → `cicids2017_0` → `cicids2017_2` → `cicids2017_5` → `cicids2017_1`
`generalization_asc`	`cicids2017_1` → `cicids2017_5` → `cicids2017_2` → `cicids2017_0` → `cicids2017_3` → `cicids2017_4`
`smooth_drift`	`cicids2017_5` → `cicids2017_1` → `cicids2017_4` → `cicids2017_0` → `cicids2017_2` → `cicids2017_3`
`abrupt_drift`	`cicids2017_4` → `cicids2017_5` → `cicids2017_3` → `cicids2017_1` → `cicids2017_2` → `cicids2017_0`

These orderings are intended to expose complementary continual-learning dynamics, including curriculum-like adaptation, generalization-oriented ordering, smooth drift, and abrupt drift.

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Total file size:

733 MB