The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
label: string
files: list<item: struct<file: string, bytes: int64, sha256: string>>
child 0, item: struct<file: string, bytes: int64, sha256: string>
child 0, file: string
child 1, bytes: int64
child 2, sha256: string
status: string
terminal_wrong: int64
gamma: int64
frontend_parameters: int64
causal_prefix_invariance: bool
wait_cost_per_new_payload: double
source: string
seed: int64
transition_counts: struct<expert: struct<transitions: int64, actions: list<item: int64>, mean_initial_return: double>, (... 185 chars omitted)
child 0, expert: struct<transitions: int64, actions: list<item: int64>, mean_initial_return: double>
child 0, transitions: int64
child 1, actions: list<item: int64>
child 0, item: int64
child 2, mean_initial_return: double
child 1, medium: struct<transitions: int64, actions: list<item: int64>, mean_initial_return: double>
child 0, transitions: int64
child 1, actions: list<item: int64>
child 0, item: int64
child 2, mean_initial_return: double
child 2, random: struct<transitions: int64, actions: list<item: int64>, mean_initial_return: double>
child 0, transitions: int64
child 1, actions: list<item: int64>
child 0, item: int64
child 2, mean_initial_return: double
features: string
terminal_correct: int64
to
{'status': Value('string'), 'source': Value('string'), 'features': Value('string'), 'frontend_parameters': Value('int64'), 'causal_prefix_invariance': Value('bool'), 'transition_counts': {'expert': {'transitions': Value('int64'), 'actions': List(Value('int64')), 'mean_initial_return': Value('float64')}, 'medium': {'transitions': Value('int64'), 'actions': List(Value('int64')), 'mean_initial_return': Value('float64')}, 'random': {'transitions': Value('int64'), 'actions': List(Value('int64')), 'mean_initial_return': Value('float64')}}, 'wait_cost_per_new_payload': Value('float64'), 'terminal_correct': Value('int64'), 'terminal_wrong': Value('int64'), 'gamma': Value('int64'), 'seed': Value('int64')}
because column names don't match
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 361, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2381, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2309, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
label: string
files: list<item: struct<file: string, bytes: int64, sha256: string>>
child 0, item: struct<file: string, bytes: int64, sha256: string>
child 0, file: string
child 1, bytes: int64
child 2, sha256: string
status: string
terminal_wrong: int64
gamma: int64
frontend_parameters: int64
causal_prefix_invariance: bool
wait_cost_per_new_payload: double
source: string
seed: int64
transition_counts: struct<expert: struct<transitions: int64, actions: list<item: int64>, mean_initial_return: double>, (... 185 chars omitted)
child 0, expert: struct<transitions: int64, actions: list<item: int64>, mean_initial_return: double>
child 0, transitions: int64
child 1, actions: list<item: int64>
child 0, item: int64
child 2, mean_initial_return: double
child 1, medium: struct<transitions: int64, actions: list<item: int64>, mean_initial_return: double>
child 0, transitions: int64
child 1, actions: list<item: int64>
child 0, item: int64
child 2, mean_initial_return: double
child 2, random: struct<transitions: int64, actions: list<item: int64>, mean_initial_return: double>
child 0, transitions: int64
child 1, actions: list<item: int64>
child 0, item: int64
child 2, mean_initial_return: double
features: string
terminal_correct: int64
to
{'status': Value('string'), 'source': Value('string'), 'features': Value('string'), 'frontend_parameters': Value('int64'), 'causal_prefix_invariance': Value('bool'), 'transition_counts': {'expert': {'transitions': Value('int64'), 'actions': List(Value('int64')), 'mean_initial_return': Value('float64')}, 'medium': {'transitions': Value('int64'), 'actions': List(Value('int64')), 'mean_initial_return': Value('float64')}, 'random': {'transitions': Value('int64'), 'actions': List(Value('int64')), 'mean_initial_return': Value('float64')}}, 'wait_cost_per_new_payload': Value('float64'), 'terminal_correct': Value('int64'), 'terminal_wrong': Value('int64'), 'gamma': Value('int64'), 'seed': Value('int64')}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
CIC packet annotations and training snapshots
Exact processed data used by NID_With_Laya. This repository stores prepared NumPy caches, labels and immutable split identities. It does not contain the raw multi-hundred-GB PCAP captures or model weights.
Available training snapshots
| Directory | Task and contents |
|---|---|
training_sets/cic_wait_stratified/ |
Frozen CIC WAIT connection-group experiment: 35,000 initial training targets, 10,589 validation targets, 50,000 natural-test targets (2,395 attacks), and separate WAIT diagnostics. Includes the complete first-32-payload packet caches, episode definitions, exact target IDs, train-only normalization, and 61,322 expanded training states. |
training_sets/cic_repaired_laya/ |
Earlier fixed-current-packet experiment: 120,000 targets, past-only context up to 16 packets, exact group split, frozen per-day weak labels and rule snapshot. |
training_sets/cic_baseline_frontend/ |
Frozen train-learned t384d4 packet projections and expert/medium/random offline trajectories, shared by the CIC DT/DNN/RCBC/CQL experiments. These vectors do not include Laya Transformer decision features. |
Label meaning and scope
The target is whether the marked packet itself carries an attack action; a normal server reply can provide evidence about an earlier target. Labels are rule/session-derived weak supervision, not independent human packet ground truth. A WAIT target stays fixed when later packets arrive. Unknown labels are not silently treated as benign.
These snapshots predate the ongoing MNWU re-annotation. They reproduce the reported frozen experiments; preliminary/current annotation work is not substituted for the original training labels. The Heartbleed same-session time-block exception is documented in HEARTBLEED_TIME_SPLIT.json; other connection groups are isolated, but one attack run may span different connections.
Download and use
from huggingface_hub import snapshot_download
snapshot_download(repo_id="JunJiaGuo/CIC_packet_anno", repo_type="dataset",
local_dir="cic_training_download",
allow_patterns=["training_sets/cic_wait_stratified/*"])
Use the project's publication/restore_assets.py to restore the expected local paths. These are native experiment caches (numpy.load / memory mapping), not a Hugging Face datasets.load_dataset table. File hashes, local source paths and publication completion are recorded in training_manifest.json and PUBLICATION_STATUS.json.
Provenance
Source traffic is CIC-IDS2017 (Canadian Institute for Cybersecurity, University of New Brunswick). Flow repairs reference Liu, Engelen, Lynar, Essam and Joosen, Error Prevalence in NIDS Datasets, IEEE CNS 2022: https://intrusion-detection.distrinet-research.be/CNS2022/index.html . Packet-level labels and split construction are local project derivatives. Retain the upstream dataset's attribution/usage terms when using or redistributing derived traffic. See the source code and experiment protocols for limitations.
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