Update README.md

README.md

@@ -21,4 +21,37 @@ metrics:
 - accuracy
 base_model:
 - microsoft/codebert-base
----
+---
+
+# 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)