Datasets:
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Error code: FeaturesError Exception: UnicodeDecodeError Message: 'utf-8' codec can't decode byte 0xa0 in position 356: invalid start byte Traceback: Traceback (most recent call last): File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 233, in compute_first_rows_from_streaming_response iterable_dataset = iterable_dataset._resolve_features() File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2998, in _resolve_features features = _infer_features_from_batch(self.with_format(None)._head()) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1918, in _head return _examples_to_batch(list(self.take(n))) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2093, in __iter__ for key, example in ex_iterable: File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1576, in __iter__ for key_example in islice(self.ex_iterable, self.n - ex_iterable_num_taken): File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 279, in __iter__ for key, pa_table in self.generate_tables_fn(**gen_kwags): File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/csv/csv.py", line 188, in _generate_tables csv_file_reader = pd.read_csv(file, iterator=True, dtype=dtype, **self.config.pd_read_csv_kwargs) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/streaming.py", line 75, in wrapper return function(*args, download_config=download_config, **kwargs) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 1212, in xpandas_read_csv return pd.read_csv(xopen(filepath_or_buffer, "rb", download_config=download_config), **kwargs) File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/parsers/readers.py", line 1026, in read_csv return _read(filepath_or_buffer, kwds) File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/parsers/readers.py", line 620, in _read parser = TextFileReader(filepath_or_buffer, **kwds) File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/parsers/readers.py", line 1620, in __init__ self._engine = self._make_engine(f, self.engine) File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/parsers/readers.py", line 1898, in _make_engine return mapping[engine](f, **self.options) File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/parsers/c_parser_wrapper.py", line 93, in __init__ self._reader = parsers.TextReader(src, **kwds) File "parsers.pyx", line 574, in pandas._libs.parsers.TextReader.__cinit__ File "parsers.pyx", line 663, in pandas._libs.parsers.TextReader._get_header File "parsers.pyx", line 874, in pandas._libs.parsers.TextReader._tokenize_rows File "parsers.pyx", line 891, in pandas._libs.parsers.TextReader._check_tokenize_status File "parsers.pyx", line 2053, in pandas._libs.parsers.raise_parser_error UnicodeDecodeError: 'utf-8' codec can't decode byte 0xa0 in position 356: invalid start byte
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model_details:
description: A comprehensive database and analysis tool for cyber exploits, vulnerabilities, and related information. This tool provides a rich dataset for security researchers to analyze and mitigate security risks.
task_categories:
- data_analysis
structure:
- data/
- exploits.csv
- vulnerabilities.csv
- assets/
- favicon.svg
- .streamlit/
- config.toml
- main.py
- data_processor.py
- visualizations.py
- README.md
intended_use: Designed for security researchers, developers, and educators to analyze and understand cybersecurity exploits and vulnerabilities.
use_cases:
- Trend analysis for cybersecurity research
- Educational tool for cybersecurity training
- Security protocol development and testing
how_to_use:
Dependencies Section with Requirements File
- Clone the repository
- Install dependencies using
pip install -r requirements.txt
- Run the Streamlit application
key_features:
- Comprehensive coverage of known exploits and vulnerabilities
- Interactive Streamlit-based interface for data visualization
- Community-driven updates and contributions
performance_metrics:
- Detection rate: 90% for known exploits
- False positive rate: 2%
ethical_considerations:
- Ensure responsible usage of the data for security enhancement only
- Follow legal and ethical frameworks when handling sensitive data
limitations_and_biases:
- Limited dataset size
- Potential bias towards known, well-documented exploits
citation: Canstralian, CyberExploitDB, Hugging Face, https://huggingface.co/datasets/Canstralian/CyberExploitDB
contributing: Contributions are encouraged. Refer to the CONTRIBUTING.md file for guidelines.
authors: Canstralian
acknowledgments: Thanks to contributors and supporters for their assistance.
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