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lumicero
/
xids-dataset

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The dataset generation failed because of a cast error
Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 16 new columns ({'ct_ftp_cmd', 'Dload', 'load_ratio', 'Label', 'Dintpkt', 'Djit', 'Sjit', 'Sintpkt', 'pkt_jit_interaction_dst', 'Sload', 'Stime', 'attack_cat', 'Spkts', 'dloss', 'inter_pkt_ratio', 'sloss'}) and 30 missing columns ({'loss_ratio_dst', 'total_bytes', 'ltime_weekday', 'sload', 'label', 'src_bps', 'sport', 'mean_pkt_size', 'service_src_ratio', 'dsport', 'ltime_hour', 'dintpkt', 'stime_weekday', 'byte_pkt_interaction_src', 'dload', 'attack_cat_enc', 'window_ratio', 'djit', 'sintpkt', 'spkts', 'src_pps', 'loss_ratio_src', 'sjit', 'stime_hour', 'byte_pkt_interaction_dst', 'total_pkts', 'service_dst_ratio', 'res_bdy_rate', 'dst_bps', 'tcprtt'}).

This happened while the csv dataset builder was generating data using

hf://datasets/lumicero/xids-dataset/preprocess/train_df_reduced.csv (at revision d4d581081b6d87960646da895a31cbac9c2a12e1)

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1831, in _prepare_split_single
                  writer.write_table(table)
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 714, in write_table
                  pa_table = table_cast(pa_table, self._schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2272, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              dur: double
              sbytes: double
              dbytes: double
              sttl: double
              dttl: double
              sloss: double
              dloss: double
              Sload: double
              Dload: double
              Spkts: double
              swin: double
              stcpb: int64
              dtcpb: int64
              smeansz: double
              dmeansz: double
              trans_depth: double
              res_bdy_len: double
              Sjit: double
              Djit: double
              Stime: int64
              Sintpkt: double
              Dintpkt: double
              is_sm_ips_ports: double
              ct_state_ttl: double
              ct_flw_http_mthd: double
              is_ftp_login: double
              ct_ftp_cmd: double
              ct_srv_src: double
              ct_srv_dst: double
              ct_dst_ltm: double
              ct_src__ltm: double
              ct_src_dport_ltm: double
              ct_dst_sport_ltm: double
              ct_dst_src_ltm: double
              attack_cat: int64
              Label: double
              duration: double
              byte_ratio: double
              pkt_ratio: double
              load_ratio: double
              jit_ratio: double
              inter_pkt_ratio: double
              load_jit_interaction_src: double
              load_jit_interaction_dst: double
              pkt_jit_interaction_src: double
              pkt_jit_interaction_dst: double
              tcp_seq_diff: int64
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 5908
              to
              {'sport': Value('int64'), 'dsport': Value('int64'), 'dur': Value('float64'), 'sbytes': Value('int64'), 'dbytes': Value('int64'), 'sttl': Value('int64'), 'dttl': Value('int64'), 'sload': Value('float64'), 'dload': Value('float64'), 'spkts': Value('int64'), 'swin': Value('int64'), 'stcpb': Value('int64'), 'dtcpb': Value('int64'), 'smeansz': Value('int64'), 'dmeansz': Value('int64'), 'trans_depth': Value('int64'), 'res_bdy_len': Value('int64'), 'sjit': Value('float64'), 'djit': Value('float64'), 'sintpkt': Value('float64'), 'dintpkt': Value('float64'), 'tcprtt': Value('float64'), 'is_sm_ips_ports': Value('int64'), 'ct_state_ttl': Value('int64'), 'ct_flw_http_mthd': Value('int64'), 'is_ftp_login': Value('int64'), 'ct_srv_src': Value('int64'), 'ct_srv_dst': Value('int64'), 'ct_dst_ltm': Value('int64'), 'ct_src__ltm': Value('int64'), 'ct_src_dport_ltm': Value('int64'), 'ct_dst_sport_ltm': Value('int64'), 'ct_dst_src_ltm': Value('int64'), 'label': Value('int64'), 'duration': Value('float64'), 'src_bps': Value('float64'), 'dst_bps': Value('float64'), 'src_pps': Value('float64'), 'byte_ratio': Value('float64'), 'pkt_ratio': Value('float64'), 'jit_ratio': Value('float64'), 'loss_ratio_src': Value('float64'), 'loss_ratio_dst': Value('float64'), 'window_ratio': Value('float64'), 'total_bytes': Value('int64'), 'total_pkts': Value('int64'), 'byte_pkt_interaction_src': Value('int64'), 'byte_pkt_interaction_dst': Value('int64'), 'load_jit_interaction_src': Value('float64'), 'load_jit_interaction_dst': Value('float64'), 'pkt_jit_interaction_src': Value('float64'), 'mean_pkt_size': Value('float64'), 'tcp_seq_diff': Value('int64'), 'service_src_ratio': Value('float64'), 'service_dst_ratio': Value('float64'), 'res_bdy_rate': Value('float64'), 'stime_hour': Value('int64'), 'stime_weekday': Value('int64'), 'ltime_hour': Value('int64'), 'ltime_weekday': Value('int64'), 'attack_cat_enc': Value('int64')}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1450, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 993, in stream_convert_to_parquet
                  builder._prepare_split(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1702, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1833, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 16 new columns ({'ct_ftp_cmd', 'Dload', 'load_ratio', 'Label', 'Dintpkt', 'Djit', 'Sjit', 'Sintpkt', 'pkt_jit_interaction_dst', 'Sload', 'Stime', 'attack_cat', 'Spkts', 'dloss', 'inter_pkt_ratio', 'sloss'}) and 30 missing columns ({'loss_ratio_dst', 'total_bytes', 'ltime_weekday', 'sload', 'label', 'src_bps', 'sport', 'mean_pkt_size', 'service_src_ratio', 'dsport', 'ltime_hour', 'dintpkt', 'stime_weekday', 'byte_pkt_interaction_src', 'dload', 'attack_cat_enc', 'window_ratio', 'djit', 'sintpkt', 'spkts', 'src_pps', 'loss_ratio_src', 'sjit', 'stime_hour', 'byte_pkt_interaction_dst', 'total_pkts', 'service_dst_ratio', 'res_bdy_rate', 'dst_bps', 'tcprtt'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/lumicero/xids-dataset/preprocess/train_df_reduced.csv (at revision d4d581081b6d87960646da895a31cbac9c2a12e1)
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)

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sport
int64
dsport
int64
dur
float64
sbytes
int64
dbytes
int64
sttl
int64
dttl
int64
sload
float64
dload
float64
spkts
int64
swin
int64
stcpb
int64
dtcpb
int64
smeansz
int64
dmeansz
int64
trans_depth
int64
res_bdy_len
int64
sjit
float64
djit
float64
sintpkt
float64
dintpkt
float64
tcprtt
float64
is_sm_ips_ports
int64
ct_state_ttl
int64
ct_flw_http_mthd
int64
is_ftp_login
int64
ct_srv_src
int64
ct_srv_dst
int64
ct_dst_ltm
int64
ct_src__ltm
int64
ct_src_dport_ltm
int64
ct_dst_sport_ltm
int64
ct_dst_src_ltm
int64
label
int64
duration
float64
src_bps
float64
dst_bps
float64
src_pps
float64
byte_ratio
float64
pkt_ratio
float64
jit_ratio
float64
loss_ratio_src
float64
loss_ratio_dst
float64
window_ratio
float64
total_bytes
int64
total_pkts
int64
byte_pkt_interaction_src
int64
byte_pkt_interaction_dst
int64
load_jit_interaction_src
float64
load_jit_interaction_dst
float64
pkt_jit_interaction_src
float64
mean_pkt_size
float64
tcp_seq_diff
int64
service_src_ratio
float64
service_dst_ratio
float64
res_bdy_rate
float64
stime_hour
int64
stime_weekday
int64
ltime_hour
int64
ltime_weekday
int64
attack_cat_enc
int64
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End of preview.

X-IDS Dataset & Artifacts Repository

This repository contains all the data assets, experiment results, and preprocessing steps used in the development of the X-IDS system — an Explainable Intrusion Detection System using autoencoders, LightGBM classifiers, and fine-tune T5-small text generation.

The repository includes: raw and processed data, tensor-formatted datasets for model training, and hyperparameter search results using Optuna.

Folder Structure

origin/

Raw Dataset

  • Contains the original UNSW-NB15 data files used as the base for all processing.
  • Format: .csv

preprocess/

Processed Feature Files

  • Cleaned and scaled versions of the raw data
  • Label columns transformed into explainable text format
  • Used as input to model training (LGBM, T5)

tensor/

PyTorch Tensor Data

  • Pre-tokenized and vectorized datasets saved as .pt files
  • These are ready-to-load tensors used directly in training T5 models for text generation (normal and attack explanation)

optuna/

Hyperparameter Search Logs

  • Includes .db or .sql files from Optuna optimization trials
  • Best hyperparameters saved in JSON format
  • Used to tune LightGBM and possibly T5 generation parameters

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