Datasets:

anonymizeddb
/

MCAD-CIC-3xN

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:dataSocialImpact: string
conformsTo: 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 
...
hild 0, @id: string
                  child 1, extract: struct<column: string>
                      child 0, column: string
              child 5, references: struct<field: struct<@id: string>>
                  child 0, field: struct<@id: string>
                      child 0, @id: string
              child 6, repeated: bool
      child 5, key: struct<@id: string>
          child 0, @id: string
      child 6, data: list<item: struct<orderings/name: string, orderings/task_sequence: list<item: string>>>
          child 0, item: struct<orderings/name: string, orderings/task_sequence: list<item: string>>
              child 0, orderings/name: string
              child 1, orderings/task_sequence: list<item: string>
                  child 0, item: string
name: string
rai:personalSensitiveInformation: string
license: string
version: string
citeAs: 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
description: string
prov:wasGeneratedBy: string
keywords: 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': List(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:dataSocialImpact: string
              conformsTo: 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 
              ...
              hild 0, @id: string
                                child 1, extract: struct<column: string>
                                    child 0, column: string
                            child 5, references: struct<field: struct<@id: string>>
                                child 0, field: struct<@id: string>
                                    child 0, @id: string
                            child 6, repeated: bool
                    child 5, key: struct<@id: string>
                        child 0, @id: string
                    child 6, data: list<item: struct<orderings/name: string, orderings/task_sequence: list<item: string>>>
                        child 0, item: struct<orderings/name: string, orderings/task_sequence: list<item: string>>
                            child 0, orderings/name: string
                            child 1, orderings/task_sequence: list<item: string>
                                child 0, item: string
              name: string
              rai:personalSensitiveInformation: string
              license: string
              version: string
              citeAs: 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
              description: string
              prov:wasGeneratedBy: string
              keywords: 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': List(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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MCAD-CIC-3xN

Dataset Summary

MCAD-CIC-3xN is a multi-source continual anomaly detection benchmark scenario for network intrusion detection. It combines three CIC-family source datasets into a 13-task continual-learning scenario:

CIC-IDS2017-derived tasks;
CIC-IDS2018-derived tasks;
CIC-UNSW-NB15-derived tasks.

Unlike a single-task-per-source construction, this benchmark provides multiple concept-grouped tasks per source dataset. It is intended to evaluate continual anomaly detection methods under both intra-source task variation and cross-source distribution shift.

The dataset contains 3,581,792 samples, 13 tasks, and has a reported 27.36% 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 anomaly detection datasets;
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 Sources

MCAD-CIC-3xN is derived from the following source datasets:

CIC-IDS2017: https://www.unb.ca/cic/datasets/ids-2017.html
CIC-IDS2018: https://www.unb.ca/cic/datasets/ids-2018.html
CIC-UNSW-NB15: https://www.unb.ca/cic/datasets/cic-unsw-nb15.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`, `cicids2018_2`, or `cicunsw_3`.
`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 13 tasks are grouped by source dataset:

Source group	Task identifiers
CIC-IDS2017	`cicids2017_0`, `cicids2017_1`, `cicids2017_2`, `cicids2017_3`, `cicids2017_4`
CIC-IDS2018	`cicids2018_0`, `cicids2018_1`, `cicids2018_2`, `cicids2018_3`
CIC-UNSW-NB15	`cicunsw_0`, `cicunsw_1`, `cicunsw_2`, `cicunsw_3`

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:

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
Packet Length Mean
Packet Length Std
Packet Length Variance
SYN Flag Count
ACK Flag Count
RST Flag Count
PSH Flag Count
Destination Port

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 13 tasks.

Ordering	Task sequence
`curriculum_asc`	`cicunsw_3` → `cicids2017_1` → `cicids2017_4` → `cicids2017_0` → `cicunsw_1` → `cicids2017_2` → `cicids2018_2` → `cicids2017_3` → `cicunsw_0` → `cicunsw_2` → `cicids2018_3` → `cicids2018_1` → `cicids2018_0`
`curriculum_desc`	`cicids2018_0` → `cicids2018_1` → `cicids2018_3` → `cicunsw_2` → `cicunsw_0` → `cicids2017_3` → `cicids2018_2` → `cicids2017_2` → `cicunsw_1` → `cicids2017_0` → `cicids2017_4` → `cicids2017_1` → `cicunsw_3`
`generalization_desc`	`cicunsw_3` → `cicunsw_1` → `cicunsw_0` → `cicids2017_4` → `cicunsw_2` → `cicids2018_0` → `cicids2018_2` → `cicids2017_1` → `cicids2018_1` → `cicids2017_3` → `cicids2017_2` → `cicids2017_0` → `cicids2018_3`
`generalization_asc`	`cicids2018_3` → `cicids2017_0` → `cicids2017_2` → `cicids2017_3` → `cicids2018_1` → `cicids2017_1` → `cicids2018_2` → `cicids2018_0` → `cicunsw_2` → `cicids2017_4` → `cicunsw_0` → `cicunsw_1` → `cicunsw_3`
`smooth_drift`	`cicids2018_2` → `cicids2018_3` → `cicunsw_0` → `cicids2018_1` → `cicids2017_4` → `cicunsw_1` → `cicunsw_3` → `cicunsw_2` → `cicids2018_0` → `cicids2017_0` → `cicids2017_3` → `cicids2017_1` → `cicids2017_2`
`abrupt_drift`	`cicunsw_0` → `cicids2017_2` → `cicunsw_3` → `cicids2017_1` → `cicunsw_2` → `cicids2018_2` → `cicids2017_4` → `cicids2017_0` → `cicunsw_1` → `cicids2017_3` → `cicids2018_1` → `cicids2018_3` → `cicids2018_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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