Create README.md

README.md

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+
+---
+task_categories:
+- tabular-classification
+- other
+tags:
+- security
+- android
+- malware
+- cybersecurity
+- int8
+size_categories:
+- 100K<n<1M
+license: cc-by-4.0
+language:
+- en
+pretty_name: MH-100K Android Malware Dataset
+---
+
+# MH-100K: A Comprehensive Android Malware Dataset
+
+## Dataset Summary
+
+**MH-100K** is a large-scale dataset for Android malware detection research. It contains **101,975** Android applications (APKs) collected between **2010 and 2022**, providing a diverse set of samples to study malware evolution over more than a decade.
+
+The dataset features high-dimensional tabular data representing the static analysis of these applications. It includes permissions, API calls, and intents, along with extensive metadata and detection labels derived from VirusTotal.
+
+## Dataset Structure
+
+The repository contains the dataset in a consolidated, efficient format:
+
+- **`mh100.parquet`**: The main dataset file containing the feature matrix and metadata for all 101,975 instances. Stored in `int8` format for efficiency.
+- **`mh100-labels.csv`**: Contains the label information (Malware vs Benign) and VirusTotal metadata.
+- **`feature_names.csv`**: A mapping file that lists the names of the features corresponding to the columns in the feature matrix.
+
+## How to Use
+
+You can load this dataset directly using the Hugging Face `datasets` library. 
+
+### Quick Load
+
+```python
+from datasets import load_dataset
+import pandas as pd
+
+# Load the main parquet file
+dataset = load_dataset("hendriow/mh100k", data_files="mh100.parquet", split="train")
+
+# Example: Inspect the first instance
+print(dataset[0])
+```
+
+### Loading with Feature Names
+
+Since the dataset is high-dimensional (>24k features), the columns in the parquet file might be indexed. You can map them back to their real names (e.g., `android.permission.INTERNET`) using the `feature_names.csv` file.
+
+```python
+from datasets import load_dataset
+import pandas as pd
+
+# 1. Load the Data
+dataset = load_dataset("hendriow/mh100k", data_files="mh100.parquet", split="train")
+
+# 2. Load the Feature Names
+# We use pandas to read the feature mapping file directly from the repo URL or locally
+repo_url = "[https://huggingface.co/datasets/hendriow/mh100k/resolve/main/feature_names.csv](https://huggingface.co/datasets/hendriow/mh100k/resolve/main/feature_names.csv)"
+feature_map = pd.read_csv(repo_url)
+feature_names_list = feature_map['feature_name'].tolist()
+
+# 3. (Optional) Convert to Pandas to see named columns
+df = dataset.select(range(100)).to_pandas()
+df.columns = feature_names_list + ['label'] # Assuming last col is label, adjust logic as needed
+print(df.head())
+```
+
+
+## Dataset Description
+
+
+The  **MH-100K**  dataset is a comprehensive collection of Android malware information, comprising 101,975 samples. 
+-   **Data Type:** Tabular (Int8)
+-   **Time Period:** 2010 - 2022
+-   **Source:** Samples randomly selected from AndroZoo.
+
+### Features and Attributes
+
+-   SHA256 hash (APK's signature)
+-   File name
+-   Package name
+-   Android's official compilation API
+-   166 permissions
+-   24,417 API calls
+-   250 intents
+
+
+### About VirusTotal API
+
+The  [VirusTotal API](https://developers.virustotal.com/reference/overview)  is a crucial tool in this dataset's creation, known for its prowess in detecting suspicious files and URLs. Each API request yields a JSON, aiding in categorizing the APK based on its perceived threat.
+
+## Citation
+
+If you use this dataset in your research, please cite the original authors:
+
+> @article{bragancca2023android,
+  title={Android malware detection with MH-100K: An innovative dataset for advanced research},
+  author={Bragan{\c{c}}a, Hendrio and Rocha, Vanderson and Barcellos, Lucas and Souto, Eduardo and Kreutz, Diego and Feitosa, Eduardo},
+  journal={Data in Brief},
+  volume={51},
+  pages={109750},
+  year={2023},
+  publisher={Elsevier}
+}
+> 
+> @inproceedings{bragancca2023capturing,   title={Capturing the behavior
+> of android malware with mh-100k: A novel and multidimensional
+> dataset},   author={Bragan{\c{c}}a, Hendrio and Rocha, Vanderson and
+> Barcellos, Lucas Vilanova and Souto, Eduardo and Kreutz, Diego and
+> Feitosa, Eduardo},   booktitle={Simp{\'o}sio Brasileiro de
+> Seguran{\c{c}}a da Informa{\c{c}}{\~a}o e de Sistemas Computacionais
+> (SBSeg)},   pages={510--515},   year={2023},   organization={SBC} }