Create README.md

# Android Malware Detector (MobSF Companion)

## Model description
This AI model classifies Android APKs as **benign** or **malicious** (optionally by family) using features extracted during analysis with MobSF.
Its goal is to complement MobSF reports with a reproducible ML score/decision to support triage prioritization and CI/CD automation.

## Intended use

### Primary intended uses
- Enrich the MobSF pipeline: consume analysis features (permissions) and produce a risk score.
- Research/academia: benchmarking Android malware detection models.

### Out-of-scope uses
- It is not a real-time on-device antivirus.
- It does not replace manual analysis, reversing, or signature verification.
- It should not be used as the sole criterion for punitive actions (e.g., bans) without review.

## How to use (with MobSF)
MobSF can be automated via its REST API to upload, scan, and retrieve reports, which makes it possible to integrate this model as a post-scan step.

### Minimal pipeline (conceptual)
1. Run analysis in MobSF (API).
2. Retrieve `report.json` (or another artifact).
3. Extract/transform features into the format expected by the model.
4. Run inference with the model and attach the result back into the workflow (CI/CD, dashboard, etc.).

### Example code (pseudo)
```python
# Pseudocode: replace with your actual implementation.
# 1) Call MobSF API -> get report_json
# 2) features = feature_extractor(report_json)
# 3) y_hat = model.predict(features)


## spanish

## Model description
Este modelo de IA clasifica APKs Android como **benignas** o **maliciosas** (y opcionalmente por familia) usando características extraídas durante el análisis con MobSF.
El objetivo es complementar el reporte de MobSF con una puntuación/decisión ML reproducible para priorización de triage y automatización en CI/CD.

## Intended use
### Primary intended uses
- Enriquecer el pipeline de MobSF: tomar features del análisis (permisos) y producir un score de riesgo.
- Investigación/academia: benchmarking de modelos de detección de malware Android.

### Out-of-scope uses
- No es un antivirus “en tiempo real” en dispositivo.
- No sustituye análisis manual, reversing o verificación de firmas.
- No debe usarse como único criterio para acciones punitivas (p. ej., baneos) sin revisión.

## How to use (with MobSF)
MobSF puede automatizarse vía REST API para subir, escanear y obtener reportes, lo que permite integrar este modelo como un paso posterior (post-scan). [web:7]

### Minimal pipeline (conceptual)
1. Ejecutar análisis en MobSF (API).
2. Obtener `report.json` (u otro artefacto).
3. Extraer/transformar features al formato esperado por el modelo.
4. Inferir con el modelo y adjuntar el resultado al flujo (CI/CD, dashboard, etc.).

### Example code (pseudo)
```python
# Pseudocódigo: reemplaza por tu implementación real.
# 1) Llamas MobSF API -> obtienes report_json
# 2) features = feature_extractor(report_json)
# 3) y_hat = model.predict(features)

README.md

@@ -0,0 +1,24 @@
+---
+language:
+- en
+- es
+license: apache-2.0
+tags:
+- android
+- malware-detection
+- cybersecurity
+- static-analysis
+- mobile-security
+- mobsf
+- classification
+- security
+library_name: pytorch
+pipeline_tag: text-classification
+metrics:
+- f1
+- precision
+- recall
+- accuracy
+base_model:
+- microsoft/codebert-base
+---