The dataset viewer is not available for this split.
Error code: FeaturesError
Exception: ParserError
Message: Error tokenizing data. C error: Expected 28 fields in line 6, saw 36
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 247, in compute_first_rows_from_streaming_response
iterable_dataset = iterable_dataset._resolve_features()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 4196, in _resolve_features
features = _infer_features_from_batch(self.with_format(None)._head())
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2533, in _head
return next(iter(self.iter(batch_size=n)))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2711, in iter
for key, pa_table in ex_iterable.iter_arrow():
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2249, in _iter_arrow
yield from 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/csv/csv.py", line 198, in _generate_tables
for batch_idx, df in enumerate(csv_file_reader):
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/pandas/io/parsers/readers.py", line 1843, in __next__
return self.get_chunk()
^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/pandas/io/parsers/readers.py", line 1985, in get_chunk
return self.read(nrows=size)
^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/pandas/io/parsers/readers.py", line 1923, in read
) = self._engine.read( # type: ignore[attr-defined]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/pandas/io/parsers/c_parser_wrapper.py", line 234, in read
chunks = self._reader.read_low_memory(nrows)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "pandas/_libs/parsers.pyx", line 850, in pandas._libs.parsers.TextReader.read_low_memory
File "pandas/_libs/parsers.pyx", line 905, in pandas._libs.parsers.TextReader._read_rows
File "pandas/_libs/parsers.pyx", line 874, in pandas._libs.parsers.TextReader._tokenize_rows
File "pandas/_libs/parsers.pyx", line 891, in pandas._libs.parsers.TextReader._check_tokenize_status
File "pandas/_libs/parsers.pyx", line 2061, in pandas._libs.parsers.raise_parser_error
pandas.errors.ParserError: Error tokenizing data. C error: Expected 28 fields in line 6, saw 36Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Dataset Card for Dataset Name
The Controller Area Network (CAN) bus plays a key role in keeping vehicles safe by enabling critical systems to communicate with each other. However, because it does not have its own security features, the CAN bus is open to cyber threats. A CAN bus intrusion detection system (IDS) is critical for automotive cybersecurity. This has made it especially important to create IDS that are not just accurate but also efficient enough to run on the limited hardware of Electronic Control Units (ECUs). Unfortunately, many current deep learning solutions for CAN intrusion detection use large and complex models that are too demanding for most automotive systems. Moreover, existing deep learning approaches need excessive computational resources that are unsuitable for resource-constrained ECUs. We propose TinyCNNCANNet, an ultra-lightweight convolutional neural network with just 13K parameters, designed to provide low-latency and resource-efficient CAN intrusion detection under experimental settings. Rather than focusing on on-vehicle deployment, this work evaluates the feasibility of lightweight CNN architectures for future real-time capable CAN intrusion detection. We comprehensively evaluate TinyCNNCANNet on four diverse datasets: CANFD 2021, CICIoV 2024, Multi-Fuzzer-CAN 2025, and SynCAN 2025. These datasets encompass nine attack types. TinyCNNCANNet achieves competitive or superior performance compared to models with 115- 300× more parameters. All architectures detect volume-based attacks (DoS, flooding, and fuzzing) most effectively. Sophisticated attacks (malfunction and fuzzer variants) challenge all models to a similar degree, regardless of complexity. TinyCNNCANNet shows superior generalization on synthetic out-of-distribution data (SynCAN 2025). It achieves 100% accuracy, while EfficientCANNet (86.82%) and MobileNetCANNet (59.33%) fail, revealing overfitting vulnerabilities in complex models. TinyCNNCANNet delivers 12- 20× faster inference (0.16-0.51 ms vs. 2.14-4.15 ms) and a 145- 383× smaller model size (0.04 MB vs. 5.81-15.32 MB). These results demonstrate the potential of TinyCNNCANNet for real-time capable CAN intrusion detection and indicate its suitability for future deployment on embedded automotive platforms.
Citation
T. -T. -H. Le, A. A. Adiputra, A. A. N. Dharmawangsa, H. Jang and H. Kim, "Lightweight CNN-Based Intrusion Detection for CAN Bus Networks," in IEEE Access, vol. 14, pp. 14870-14891, 2026, doi: 10.1109/ACCESS.2026.3654521. keywords: {Intrusion detection;Controller area networks;Accuracy;Computer architecture;Convolutional neural networks;Deep learning;Real-time systems;Transformers;Computational modeling;Complexity theory;CAN bus security;intrusion detection system;lightweight CNN;automotive cybersecurity;deep learning},
BibTeX:
@ARTICLE{11355494, author={Le, Thi-Thu-Huong and Adiputra, Andro Aprila and Dharmawangsa, Anak Agung Ngurah and Jang, Hyunjin and Kim, Howon}, journal={IEEE Access}, title={Lightweight CNN-Based Intrusion Detection for CAN Bus Networks}, year={2026}, volume={14}, number={}, pages={14870-14891}, keywords={Intrusion detection;Controller area networks;Accuracy;Computer architecture;Convolutional neural networks;Deep learning;Real-time systems;Transformers;Computational modeling;Complexity theory;CAN bus security;intrusion detection system;lightweight CNN;automotive cybersecurity;deep learning}, doi={10.1109/ACCESS.2026.3654521}}
Dataset Card Contact
Email: lehuong7885@gmail.com
- Downloads last month
- 15