Update README.md

README.md

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----
-license: bsd-2-clause
----
+---
+language:
+- en
+pretty_name: BODMAS Cleaned
+task_categories:
+- tabular-classification
+tags:
+- cybersecurity
+- malware
+- static-analysis
+- pe-files
+- malware-detection
+- malware-family-classification
+- concept-drift
+- temporal-analysis
+- tabular
+- ai-ready
+- tabular-classification
+size_categories:
+- 100K<n<1M
+---
+ 
+# BODMAS Cleaned
+ 
+BODMAS Cleaned is a cleaned and analysis-ready version of the original **BODMAS (Blue Hexagon Open Dataset for Malware Analysis)** dataset.
+ 
+The original BODMAS dataset was introduced for machine-learning-based static malware analysis on Windows Portable Executable (PE) files, with support not only for binary malware detection but also for **temporal analysis** and **malware family studies**. This cleaned release preserves that analytical value while making the data easier to load, validate, and reuse.
+ 
+Compared with the original source asset, this release removes duplicate samples, drops constant features, standardizes metadata, and preserves both timestamp and family information. Each sample is represented as a fixed-length numerical vector extracted statically from the original PE file, without executing the binary.
+ 
+## Original dataset
+ 
+This dataset is a cleaned derivative of the original BODMAS dataset:
+ 
+- **Original name:** BODMAS (Blue Hexagon Open Dataset for Malware Analysis)
+- **Original providers:** University of Illinois at Urbana-Champaign (UIUC) / Blue Hexagon
+- **Original paper:** Yang et al. (2021). *BODMAS: An Open Dataset for Learning based Temporal Analysis of PE Malware*
+- **Original paper link:** https://liminyang.web.illinois.edu/data/DLS21_BODMAS.pdf
+- **Original website/repository:** https://whyisyoung.github.io/BODMAS/
+ 
+Please cite the original BODMAS paper when using this cleaned release in research.
+ 
+## Files
+ 
+| File | Description |
+|---|---|
+| `bodmas_clean.npz` | Index file with row/feature counts and file references |
+| `bodmas_clean_X.npy` | Feature matrix (`float32`), raw memmap, shape `(134428, 2326)` |
+| `bodmas_clean_y.npy` | Label vector (`int32`), `0 = benign`, `1 = malware` |
+| `bodmas_clean_metadata.parquet` | Per-sample metadata: SHA-256, timestamps, family fields, and quality flags |
+| `manifest.json` | Versioned manifest with checksums and artifact references |
+| `BODMAS_cleaned_notebook.ipynb` | Exploration and usage notebook |
+ 
+## What’s in the dataset?
+ 
+This cleaned release contains a fully labeled static malware dataset derived from the original BODMAS collection.
+ 
+### Core contents
+ 
+- **134,428 labeled samples**
+- **2,326 numerical features**
+- feature dtype: `float32`
+- label dtype: `int32`
+- labels:
+  - `0` = benign
+  - `1` = malware
+ 
+### Label distribution
+ 
+- **77,138 benign samples**
+- **57,290 malware samples**
+ 
+### Metadata retained
+ 
+The cleaned metadata preserves the fields that make BODMAS especially useful for temporal and family-aware analysis, including:
+ 
+- `sha256`
+- `timestamp`
+- `family`
+- `record_id`
+- `sample_id`
+- `label_int`
+- `label_str`
+- `timestamp_raw`
+- `hash_type`
+- `bodmas_family`
+- `flag_sha256`
+- `flag_timestamp_missing`
+- `flag_family_missing`
+- `flag_is_benign`
+- `feature_hash`
+ 
+### Family information
+ 
+- **580 unique malware families** are present in the cleaned dataset
+- the family distribution is skewed:
+  - top 5 families cover **35.5%** of malware samples
+  - top 20 families cover **69.4%** of malware samples
+ 
+### Feature representation
+ 
+Samples are not raw executables. Each file is represented as a **fixed-length static feature vector** extracted from the original PE file. These features describe structural and statistical properties of the binary, such as:
+ 
+- PE headers
+- sections
+- imports
+- entropy-related information
+- histogram-based characteristics
+ 
+## Cleaning summary
+ 
+This release is the output of a quality-control and harmonization pipeline applied to the original BODMAS artifacts.
+ 
+Main processing steps:
+ 
+1. **Duplicate removal**
+2. **Constant-feature filtering**, reducing the feature space from 2,381 to **2,326**
+3. **Metadata standardization**
+4. **Missing-value normalization and quality flagging**
+5. **Family/label consistency checks**
+6. **Manifest generation** for reproducibility and integrity checks
+ 
+Summary of the cleaned release:
+ 
+- original source size: **134,435 labeled samples**
+- final cleaned size: **134,428 samples**
+- final feature space: **2,326 features**
+- **no separate unlabeled split**
+- timestamps and malware family metadata preserved
+ 
+In the cleaned metadata, family-label consistency is also checked using the BODMAS convention that empty family values correspond to benign samples, while non-empty family values correspond to malware samples.
+ 
+## File structure
+ 
+```text
+bodmas_cleaned/
+├── bodmas_clean.npz
+├── bodmas_clean_X.npy
+├── bodmas_clean_y.npy
+├── bodmas_clean_metadata.parquet
+└── manifest.json
+```
+ 
+The `.npz` index stores `_rows` and `_features` for reliable loading.  
+The feature matrix is a raw memmap-backed array and should be loaded with explicit dtype and shape.
+ 
+Unlike EMBER Cleaned, there is **no separate unlabeled split** in BODMAS Cleaned.
+ 
+## Requirements
+ 
+To run the quickstart examples, install the minimum required dependencies:
+ 
+```bash
+pip install numpy pandas pyarrow
+```
+ 
+For notebook-based exploration and basic visualization, you may also install:
+ 
+```bash
+pip install jupyter matplotlib seaborn scikit-learn
+```
+ 
+## Quickstart
+ 
+This example loads the labeled BODMAS Cleaned dataset and checks that features, labels, and metadata are consistent and ready for supervised use.
+ 
+```python
+import numpy as np
+import pandas as pd
+ 
+idx = np.load("bodmas_clean.npz", allow_pickle=True)
+n_rows = int(idx["_rows"])
+n_features = int(idx["_features"])
+ 
+X = np.fromfile("bodmas_clean_X.npy", dtype=np.float32)
+assert X.size == n_rows * n_features, (
+    f"Unexpected X size: got {X.size}, expected {n_rows * n_features}"
+)
+X = X.reshape(n_rows, n_features)
+ 
+meta = pd.read_parquet("bodmas_clean_metadata.parquet")
+y = meta["label_int"].to_numpy(dtype=np.int32, copy=False)
+ 
+print(
+    f"Dataset: {X.shape[0]} samples, {X.shape[1]} features | "
+    f"labels: {len(y)} | metadata columns: {meta.shape[1]}"
+)
+ 
+assert X.shape[0] == len(y) == len(meta)
+assert set(np.unique(y)) == {0, 1}
+ 
+print("Unique labels:", np.unique(y))
+print("Metadata columns:", meta.columns.tolist())
+print("All checks passed.")
+```
+ 
+## Notebook
+ 
+The repository also includes an exploration notebook in `.ipynb` format, designed to provide additional context on the cleaned dataset, its structure, and its main analytical use cases.
+ 
+The notebook can be used to:
+ 
+- inspect the labeled dataset
+- explore metadata fields, timestamps, and family distributions
+- validate dataset consistency
+- review temporal and family-aware analyses
+- explore example downstream use cases
+ 
+To open it locally, run:
+ 
+```bash
+jupyter notebook BODMAS_cleaned_notebook.ipynb
+```
+ 
+or, if you use JupyterLab:
+ 
+```bash
+jupyter lab BODMAS_cleaned_notebook.ipynb
+```
+ 
+Make sure to open the notebook from the dataset root directory so that relative file paths resolve correctly.
+ 
+ 
+## Typical use cases
+ 
+BODMAS Cleaned supports:
+ 
+- binary malware detection
+- malware family classification
+- temporal analysis
+- concept drift studies
+- time-aware validation
+- family distribution analysis
+- feature importance analysis
+ 
+The accompanying notebook includes loading, exploratory data analysis, temporal and family analysis, and example use cases focused on discriminative signal and model evaluation.
+ 
+## Notes and limitations
+ 
+- This is a **static-analysis** dataset only.
+- The cleaned release contains **derived features**, not raw PE binaries.
+- Family names are dataset-specific and should not be treated as a universal malware ontology.
+- Temporal metadata should be respected during evaluation to avoid leakage.
+- Family distribution is concentrated, so downstream family-level evaluation should account for skew.
+- The dataset is intended for defensive research, benchmarking, and education.
+ 
+## License
+ 
+This cleaned release is derived from BODMAS. The original BODMAS data files are associated with the BSD-2 License. Please verify that your downstream redistribution and reuse remain aligned with the original BODMAS terms.
+ 
+## References
+ 
+If you use this dataset, please cite the original BODMAS paper:
+ 
+```bibtex
+@inproceedings{yang2021bodmas,
+  title={BODMAS: An Open Dataset for Learning based Temporal Analysis of PE Malware},
+  author={Yang, Limin and others},
+  booktitle={Proceedings of the 2021 ACM Workshop on Data-Limited Security Research},
+  year={2021}
+}
+```
+ 
+APA:
+ 
+Yang, L., et al. (2021). *BODMAS: An Open Dataset for Learning based Temporal Analysis of PE Malware*. In *Proceedings of the 2021 ACM Workshop on Data-Limited Security Research*.
+ 
+## Contacts
+
+- **Shared by:** ACN
+