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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 70 new columns ({'Fwd_IAT_Min', 'Flow_IAT_Std', 'Idle_Std', 'Flow_Byts/s', 'Pkt_Len_Min', 'Pkt_Len_Max', 'Fwd_IAT_Std', 'Bwd_Pkt_Len_Max', 'URG_Flag_Cnt', 'Bwd_Pkts/b_Avg', 'Init_Fwd_Win_Byts', 'Fwd_URG_Flags', 'Fwd_Seg_Size_Min', 'Bwd_IAT_Tot', 'Protocol', 'Flow_IAT_Min', 'Idle_Min', 'Bwd_IAT_Min', 'Fwd_IAT_Tot', 'Dst_Port', 'Fwd_Pkts/b_Avg', 'Active_Std', 'Pkt_Len_Mean', 'Bwd_Pkt_Len_Std', 'Pkt_Len_Std', 'Bwd_Byts/b_Avg', 'Bwd_IAT_Std', 'Dst_IP', 'Flow_Duration', 'Fwd_Pkts/s', 'Bwd_Pkt_Len_Mean', 'Fwd_Header_Len', 'Idle_Mean', 'TotLen_Fwd_Pkts', 'Bwd_URG_Flags', 'RST_Flag_Cnt', 'Src_Port', 'ACK_Flag_Cnt', 'SYN_Flag_Cnt', 'Fwd_Act_Data_Pkts', 'Down/Up_Ratio', 'Fwd_PSH_Flags', 'Fwd_Pkt_Len_Std', 'Flow_IAT_Max', 'Flow_IAT_Mean', 'Bwd_Pkts/s', 'Bwd_IAT_Mean', 'Fwd_Pkt_Len_Min', 'Fwd_Byts/b_Avg', 'Bwd_Blk_Rate_Avg', 'Bwd_Header_Len', 'Tot_Fwd_Pkts', 'Bwd_PSH_Flags', 'Bwd_Pkt_Len_Min', 'Fwd_Pkt_Len_Mean', 'Src_IP', 'Fwd_Pkt_Len_Max', 'ECE_Flag_Cnt', 'FIN_Flag_Cnt', 'Tot_Bwd_Pkts', 'Active_Min', 'Init_Bwd_Win_Byts', 'Active_Mean', 'Flow_Pkts/s', 'Flow_ID', 'Fwd_Blk_Rate_Avg', 'Fwd_IAT_Mean', 'TotLen_Bwd_Pkts', 'CWE_Flag_Count', 'Pkt_Len_Var'}) and 3 missing columns ({'DLC', 'Data', 'Arbitration_ID'}).
This happened while the csv dataset builder was generating data using
hf://datasets/Thi-Thu-Huong/resampled_IDS_datasets/resampled_train_IoTID20.csv (at revision 0320877d48042b3ec0b83e09f05e4d12db5ad6a5)
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 "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1871, in _prepare_split_single
writer.write_table(table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 623, in write_table
pa_table = table_cast(pa_table, self._schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2293, in table_cast
return cast_table_to_schema(table, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2241, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
Tot_Fwd_Pkts: int64
Fwd_Pkts/b_Avg: int64
Pkt_Len_Mean: double
Flow_IAT_Min: double
Fwd_PSH_Flags: int64
Active_Std: double
Protocol: int64
Fwd_Pkts/s: double
Bwd_Byts/b_Avg: int64
Fwd_Pkt_Len_Min: double
Fwd_IAT_Std: double
Idle_Mean: double
Flow_IAT_Mean: double
Pkt_Len_Max: double
Src_IP: double
Dst_Port: int64
Bwd_IAT_Min: double
Flow_ID: double
TotLen_Fwd_Pkts: double
Init_Fwd_Win_Byts: int64
Bwd_Pkt_Len_Min: double
Fwd_Pkt_Len_Std: double
Bwd_Pkt_Len_Max: double
Fwd_Byts/b_Avg: int64
Active_Mean: double
Bwd_Pkt_Len_Std: double
Flow_Duration: int64
TotLen_Bwd_Pkts: double
Active_Min: double
Tot_Bwd_Pkts: int64
Flow_Byts/s: double
Fwd_Pkt_Len_Mean: double
Bwd_Pkts/s: double
Pkt_Len_Var: double
Fwd_Pkt_Len_Max: double
Bwd_URG_Flags: int64
Fwd_Header_Len: int64
Bwd_IAT_Std: double
Dst_IP: double
Flow_IAT_Std: double
ECE_Flag_Cnt: int64
Pkt_Len_Std: double
Fwd_URG_Flags: int64
Flow_IAT_Max: double
CWE_Flag_Count: int64
ACK_Flag_Cnt: int64
Flow_Pkts/s: double
Bwd_Blk_Rate_Avg: int64
Idle_Std: double
Bwd_Header_Len: int64
Init_Bwd_Win_Byts: int64
FIN_Flag_Cnt: int64
Bwd_PSH_Flags: int64
Fwd_Seg_Size_Min: int64
Bwd_IAT_Tot: double
Bwd_Pkt_Len_Mean: double
URG_Flag_Cnt: int64
Fwd_Blk_Rate_Avg: int64
Src_Port: int64
RST_Flag_Cnt: int64
Fwd_Act_Data_Pkts: int64
Down/Up_Ratio: double
Bwd_Pkts/b_Avg: int64
Fwd_IAT_Min: double
Fwd_IAT_Mean: double
SYN_Flag_Cnt: int64
Timestamp: double
Idle_Min: double
Fwd_IAT_Tot: double
Pkt_Len_Min: double
Bwd_IAT_Mean: double
Classes: int64
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 9165
to
{'Timestamp': Value(dtype='float64', id=None), 'Arbitration_ID': Value(dtype='float64', id=None), 'DLC': Value(dtype='int64', id=None), 'Data': Value(dtype='float64', id=None), 'Classes': Value(dtype='int64', id=None)}
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 1438, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1050, in convert_to_parquet
builder.download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 925, in download_and_prepare
self._download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1001, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1742, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1873, 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 70 new columns ({'Fwd_IAT_Min', 'Flow_IAT_Std', 'Idle_Std', 'Flow_Byts/s', 'Pkt_Len_Min', 'Pkt_Len_Max', 'Fwd_IAT_Std', 'Bwd_Pkt_Len_Max', 'URG_Flag_Cnt', 'Bwd_Pkts/b_Avg', 'Init_Fwd_Win_Byts', 'Fwd_URG_Flags', 'Fwd_Seg_Size_Min', 'Bwd_IAT_Tot', 'Protocol', 'Flow_IAT_Min', 'Idle_Min', 'Bwd_IAT_Min', 'Fwd_IAT_Tot', 'Dst_Port', 'Fwd_Pkts/b_Avg', 'Active_Std', 'Pkt_Len_Mean', 'Bwd_Pkt_Len_Std', 'Pkt_Len_Std', 'Bwd_Byts/b_Avg', 'Bwd_IAT_Std', 'Dst_IP', 'Flow_Duration', 'Fwd_Pkts/s', 'Bwd_Pkt_Len_Mean', 'Fwd_Header_Len', 'Idle_Mean', 'TotLen_Fwd_Pkts', 'Bwd_URG_Flags', 'RST_Flag_Cnt', 'Src_Port', 'ACK_Flag_Cnt', 'SYN_Flag_Cnt', 'Fwd_Act_Data_Pkts', 'Down/Up_Ratio', 'Fwd_PSH_Flags', 'Fwd_Pkt_Len_Std', 'Flow_IAT_Max', 'Flow_IAT_Mean', 'Bwd_Pkts/s', 'Bwd_IAT_Mean', 'Fwd_Pkt_Len_Min', 'Fwd_Byts/b_Avg', 'Bwd_Blk_Rate_Avg', 'Bwd_Header_Len', 'Tot_Fwd_Pkts', 'Bwd_PSH_Flags', 'Bwd_Pkt_Len_Min', 'Fwd_Pkt_Len_Mean', 'Src_IP', 'Fwd_Pkt_Len_Max', 'ECE_Flag_Cnt', 'FIN_Flag_Cnt', 'Tot_Bwd_Pkts', 'Active_Min', 'Init_Bwd_Win_Byts', 'Active_Mean', 'Flow_Pkts/s', 'Flow_ID', 'Fwd_Blk_Rate_Avg', 'Fwd_IAT_Mean', 'TotLen_Bwd_Pkts', 'CWE_Flag_Count', 'Pkt_Len_Var'}) and 3 missing columns ({'DLC', 'Data', 'Arbitration_ID'}).
This happened while the csv dataset builder was generating data using
hf://datasets/Thi-Thu-Huong/resampled_IDS_datasets/resampled_train_IoTID20.csv (at revision 0320877d48042b3ec0b83e09f05e4d12db5ad6a5)
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)Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Timestamp
float64 | Arbitration_ID
float64 | DLC
int64 | Data
float64 | Classes
int64 |
|---|---|---|---|---|
564,420
| 50
| 4
| 238,335
| 2
|
371,275
| 12
| 8
| 9,932
| 2
|
507,895
| 41
| 8
| 146,403
| 2
|
784,621
| 8
| 8
| 156,220
| 2
|
367,796
| 9
| 8
| 204,292
| 1
|
119,756
| 0
| 8
| 0
| 0
|
756,528
| 23
| 8
| 43,774
| 2
|
301,321
| 18
| 8
| 227,055
| 2
|
97,732
| 9
| 8
| 57,819
| 2
|
253,840
| 14
| 7
| 127,830
| 2
|
74,425
| 34
| 8
| 47,064
| 2
|
368,335
| 56
| 8
| 0
| 2
|
134,724
| 57
| 8
| 67,603
| 2
|
217,778
| 50
| 4
| 169,231
| 2
|
272,312
| 19
| 8
| 57,315
| 2
|
309,282
| 23
| 8
| 44,357
| 2
|
250,613
| 12
| 8
| 275,407
| 2
|
40,489
| 8
| 8
| 218,938
| 2
|
328,542
| 13
| 8
| 10,191
| 2
|
709,494
| 27
| 8
| 277,738
| 2
|
37,804
| 10
| 6
| 206,774
| 2
|
13,871
| 17
| 8
| 194,625
| 2
|
575,221
| 13
| 8
| 10,169
| 2
|
115,269
| 31
| 5
| 42,573
| 2
|
146,732
| 16
| 8
| 13,614
| 2
|
443,417
| 71
| 8
| 0
| 2
|
309,522
| 4
| 8
| 150,174
| 2
|
298,386
| 26
| 8
| 144,793
| 2
|
704,472
| 19
| 8
| 174,890
| 2
|
588,505
| 9
| 8
| 60,129
| 2
|
11,834
| 34
| 8
| 48,719
| 2
|
664,808
| 3
| 8
| 153,263
| 2
|
528,830
| 34
| 8
| 47,533
| 2
|
767,058
| 5
| 8
| 106,350
| 2
|
764,218
| 10
| 6
| 128,823
| 2
|
139,080
| 15
| 8
| 1,962
| 2
|
132,697
| 26
| 8
| 238,257
| 2
|
625,986
| 22
| 8
| 11
| 2
|
470,542
| 38
| 8
| 10,955
| 2
|
667,701
| 3
| 8
| 213,944
| 2
|
437,216
| 28
| 8
| 81
| 2
|
297,912
| 42
| 8
| 10,912
| 2
|
3,745
| 5
| 8
| 106,353
| 2
|
797,743
| 50
| 4
| 144,613
| 2
|
87,090
| 10
| 6
| 84,040
| 2
|
67,276
| 22
| 8
| 14
| 2
|
754,948
| 9
| 8
| 56,798
| 2
|
270,237
| 6
| 4
| 18,387
| 2
|
472,343
| 0
| 8
| 0
| 0
|
684,690
| 12
| 8
| 275,407
| 2
|
179,189
| 10
| 6
| 89,228
| 2
|
332,398
| 69
| 8
| 0
| 2
|
806,226
| 9
| 8
| 49,527
| 2
|
465,103
| 13
| 8
| 10,187
| 2
|
21,404
| 27
| 8
| 277,684
| 2
|
481,467
| 0
| 8
| 0
| 0
|
389,988
| 14
| 7
| 142,359
| 2
|
670,152
| 13
| 8
| 10,218
| 2
|
722,826
| 9
| 8
| 51,492
| 2
|
291,657
| 11
| 8
| 81,724
| 2
|
465,324
| 10
| 6
| 208,493
| 2
|
586,205
| 10
| 6
| 152,819
| 2
|
588,139
| 31
| 5
| 42,849
| 2
|
553,959
| 18
| 8
| 97,426
| 2
|
491,585
| 0
| 8
| 0
| 0
|
736,866
| 3
| 8
| 115,767
| 2
|
115,436
| 12
| 8
| 275,410
| 2
|
796,084
| 27
| 8
| 277,683
| 2
|
420,253
| 26
| 8
| 256,571
| 2
|
641,661
| 14
| 7
| 128,462
| 2
|
408,776
| 27
| 8
| 277,739
| 2
|
543,362
| 13
| 8
| 10,171
| 2
|
37,349
| 7
| 8
| 75,852
| 2
|
204,119
| 63
| 8
| 11,203
| 2
|
78,253
| 6
| 4
| 18,660
| 2
|
495,833
| 0
| 8
| 0
| 0
|
795,186
| 4
| 8
| 120,088
| 2
|
758,286
| 8
| 8
| 72,121
| 2
|
483,813
| 0
| 8
| 0
| 0
|
580,338
| 20
| 8
| 9,936
| 2
|
120,770
| 0
| 8
| 0
| 0
|
193,501
| 34
| 8
| 47,344
| 2
|
707,219
| 15
| 8
| 5,825
| 2
|
444,697
| 16
| 8
| 16,925
| 2
|
432,845
| 9
| 8
| 63,331
| 2
|
4,797
| 50
| 4
| 104,376
| 2
|
472,678
| 11
| 8
| 205,429
| 2
|
572,300
| 16
| 8
| 30,756
| 2
|
50,095
| 3
| 8
| 257,258
| 2
|
155,951
| 14
| 7
| 147,194
| 2
|
672,976
| 32
| 8
| 89,894
| 2
|
532,962
| 13
| 8
| 10,199
| 2
|
549,797
| 19
| 8
| 210,808
| 2
|
68,001
| 50
| 4
| 238,151
| 2
|
3,253
| 23
| 8
| 43,459
| 2
|
776,466
| 5
| 8
| 106,352
| 2
|
330,263
| 5
| 8
| 106,351
| 2
|
101,244
| 0
| 8
| 0
| 0
|
409,774
| 12
| 8
| 275,399
| 2
|
466,091
| 6
| 4
| 18,520
| 2
|
Dataset Card for resampled_IDS_datasets
Intrusion Detection Systems (IDS) play a crucial role in securing computer networks against malicious activities. However, their efficacy is consistently hindered by the persistent challenge of class imbalance in real-world datasets. While various methods, such as resampling techniques, ensemble methods, cost-sensitive learning, data augmentation, and so on, have individually addressed imbalance classification issues, there exists a notable gap in the literature for effective hybrid methodologies aimed at enhancing IDS performance. To bridge this gap, our research introduces an innovative methodology that integrates hybrid undersampling and oversampling strategies within an ensemble classification framework. This novel approach is designed to harmonize dataset distributions and optimize IDS performance, particularly in intricate multi-class scenarios. In-depth evaluations were conducted using well-established intrusion detection datasets, including the Car Hacking: Attack and Defense Challenge 2020 (CHADC2020) and IoTID20. Our results showcase the remarkable efficacy of the proposed methodology, revealing significant improvements in precision, recall, and F1-score metrics. Notably, the hybrid-ensemble method demonstrated an exemplary average F1 score exceeding 98% for both datasets, underscoring its exceptional capability to substantially enhance intrusion detection accuracy. In summary, this research represents a significant contribution to the field of IDS, providing a robust solution to the pervasive challenge of class imbalance. The hybrid framework not only strengthens IDS efficacy but also illuminates the seamless integration of undersampling and oversampling within ensemble classifiers, paving the way for fortified network defenses.
Dataset Description
We provide resampled datasets based on BorderlineSMOTE method from a part of public dataset Car_Hacking_Challenge_Dataset and IoT Network Intrusion Dataset (IoTID20)
In the Car_Hacking_Challenge_Dataset, we labelled output classes as 'Flooding' as 0 , 'Fuzzing' as 1, 'Normal' as 2, 'Replay' as 3,'Spoofing' as 4.
In the IoTID20 dataset, we labelled output classes as 'DoS-Synflooding' as 0 , 'MITM ARP Spoofing' as 1, 'Mirai ARP Spoofing' as 2, 'Mirai-Hostbruteforceg' as 3, 'Mirai HTTP Flooding' as 4 , 'Mirai UDP Flooding' as 5, 'Scan Host Port' as 6, 'Scan Port OS' as 7, 'Normal' as 8.
Citation
Le, T.T.H, Shin, Y., Kim, M., & Kim, H. (2024). Towards unbalanced multiclass intrusion detection with hybrid sampling methods and ensemble classification. Applied Soft Computing, 157, 111517.
BibTeX:
@article{le2024towards, title={Towards unbalanced multiclass intrusion detection with hybrid sampling methods and ensemble classification}, author={Le, Thi Thu Huong and Shin, Yeongjae and Kim, Myeongkil and Kim, Howon and others}, journal={Applied Soft Computing}, volume={157}, pages={111517}, year={2024}, publisher={Elsevier} }
@misc {le_2025, author = { {Le} }, title = { resampled_IDS_datasets (Revision 45a8285) }, year = 2025, url = { https://huggingface.co/datasets/Thi-Thu-Huong/resampled_IDS_datasets }, doi = { 10.57967/hf/4961 }, publisher = { Hugging Face } }
Dataset Card Contact
Email: [email protected]
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