Datasets:

Xinzxr
/

MalEval

Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      JSON parse error: Invalid value. in row 0
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 324, in _generate_tables
                  df = pandas_read_json(f)
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 38, in pandas_read_json
                  return pd.read_json(path_or_buf, **kwargs)
                         ~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 791, in read_json
                  json_reader = JsonReader(
                      path_or_buf,
                  ...<16 lines>...
                      engine=engine,
                  )
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 905, in __init__
                  self.data = self._preprocess_data(data)
                              ~~~~~~~~~~~~~~~~~~~~~^^^^^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 917, in _preprocess_data
                  data = data.read()
                File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 844, in read_with_retries
                  out = read(*args, **kwargs)
                File "<frozen codecs>", line 325, in decode
              UnicodeDecodeError: 'utf-8' codec can't decode byte 0xe8 in position 14: invalid continuation byte
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 243, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 4379, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2661, in _head
                  return next(iter(self.iter(batch_size=n)))
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2839, in iter
                  for key, pa_table in ex_iterable.iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2377, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, 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 327, in _generate_tables
                  raise e
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 290, in _generate_tables
                  pa_table = paj.read_json(
                      io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
                  )
                File "pyarrow/_json.pyx", line 342, in pyarrow._json.read_json
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                  return check_status(status)
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
                  raise convert_status(status)
              pyarrow.lib.ArrowInvalid: JSON parse error: Invalid value. in row 0

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MalEval Dataset

This dataset provides the data used by MalEval, a framework for evaluating Android malware behavior reports generated by large language models. The code is available at https://github.com/ZhengXR930/MalEval.

The dataset includes the info/ metadata directory and separate archives by artifact type. Extract the archives under an artifacts/ directory before running the code.

mkdir -p artifacts
unzip apk.zip -d artifacts
unzip call_chain.zip -d artifacts
unzip entrypoints.zip -d artifacts
unzip functions.zip -d artifacts
unzip meta_info.zip -d artifacts
unzip reachable_func.zip -d artifacts
unzip reports.zip -d artifacts

After extraction, place info/ beside artifacts/. The expected layout is:

info/
|-- archived_sample_info.json
|-- benign_sample_info.json
`-- latest_sample_info.json

artifacts/
|-- apk/
|-- call_chain/
|-- entrypoints/
|-- functions/
|-- meta_info/
|-- reachable_func/
`-- reports/

Files

info/: split metadata and sample indexes used by the code repository. It contains archived_sample_info.json for MalRadar samples, latest_sample_info.json for newly collected malware samples, and benign_sample_info.json for benign apps.
apk.zip: APK files for the benign, MalRadar, and newly collected malware splits. APKs are named by SHA-256.
entrypoints.zip: static-analysis entry points extracted from each APK.
call_chain.zip: caller-callee call-chain records used to construct context-aware summaries.
functions.zip: decompiled function bodies extracted from each APK.
reachable_func.zip: reachable function sets derived from the entry points and call-chain analysis.
meta_info.zip: metadata extracted for each APK, used by metadata-aware behavior generation.
reports.zip: malware-analysis reports and parsed report annotations used as reference material for evaluation.
SHA256SUMS: SHA-256 checksums for all zip archives.

Splits

The dataset follows the split metadata in the code repository:

malradar: archived malware samples from MalRadar.
new: newly collected malware samples.
benign: benign Android applications used for false-positive evaluation.

Each static-analysis archive contains subdirectories matching these split names.

Usage

Clone the code repository, download and extract the dataset archives, then run either pipeline:

From APK: start from artifacts/apk/ and regenerate static-analysis artifacts with src/preparation/run_static_analysis.py.
From artifact: start from the precomputed entrypoints, call_chain, functions, reachable_func, and meta_info directories to generate function summaries, behavior reports, and metrics.

See the code repository README for exact commands.

Safety and Redistribution

The APK files may contain malware. Handle them only in an isolated analysis environment and do not install them on personal devices.

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