Updated README.md

README.md

@@ -36,6 +36,14 @@ McNdroid is a large-scale, longitudinal, multimodal Android malware detection da
 - **Labels:** Binary (malware/benign) and multi-vendor family-level verdicts
 
 
+### Supported Tasks
+
+- Android malware detection (binary classification)
+- Concept drift detection and temporal robustness evaluation
+- Multi-modal learning for malware analysis
+- Graph-based malware classification
+
+
 ## Dataset Structure
 
 ### Repository Layout
@@ -94,17 +102,28 @@ API call graphs stored in GML format, organized by year. Each graph represents i
 
 Behavioral feature representations stored as JSON files, organized by year.
 
+
 ## Dataset Creation
 
+### Source Data
+
+Samples were collected from public malware repositories and benign application stores spanning 2013–2025. Each sample was processed through a static analysis pipeline to extract permissions, API calls, intents, and other manifest and bytecode-level features.
+
+### Annotations
+
+Labels are derived from VirusTotal multi-scanner verdicts. The `vendor_family_wide_verdict.csv` file preserves per-vendor family attributions to support research on label noise and disagreement.
+
 
 ## Considerations for Using the Data
 
 
 ### Social Impact
 
+This dataset is intended for defensive cybersecurity research. It should not be used to develop offensive malware capabilities.
 
 ### Licensing
 
+This dataset is released under the [Creative Commons Attribution 4.0 International License (CC-BY-4.0)](https://creativecommons.org/licenses/by/4.0/).
 
 
 ## Usage
@@ -132,4 +151,15 @@ metadata = pd.read_csv("metadata.csv")
 verdicts = pd.read_csv("vendor_family_wide_verdict.csv")
 ```
 
-## Citation
+
+## Citation
+
+If you use this dataset, please cite:
+
+```bibtex
+[More Information Needed]
+```
+
+## Contact
+
+For questions or issues, please open a discussion on the [Community tab](https://huggingface.co/datasets/IQSeC-Lab/McNdroid/discussions).