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README.md

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-license: mit
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+---
+---
+title: MCMA - Malware Static Analyzer & YARA Generator
+emoji: 🛡️
+colorFrom: blue
+colorTo: red
+sdk: gradio
+sdk_version: 5.0.0
+app_file: app.py
+pinned: false
+license: mit
+tags:
+  - cybersecurity
+  - malware-analysis
+  - yara
+  - rag
+  - llm
+  - automation
+---
+
+# 🛡️ MCMA – Malware Static Analyzer
+
+**MCMA (Malware Classification & Malware Analysis)** is an AI-powered framework designed to assist security analysts and threat hunters. It performs **static analysis** on suspicious files and automatically generates **YARA rules** for detection.
+
+Using a combination of Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs), MCMA identifies suspicious patterns without executing the file, making it a safe initial step in the malware analysis pipeline.
+
+## 🚀 Key Features
+
+*   **Static Analysis:** Extracts metadata, strings, and headers without running the binary.
+*   **AI-Driven Insights:** Uses an LLM to interpret raw file data and explain *why* a file looks suspicious.
+*   **Auto-YARA Generation:** Automatically writes syntactically correct YARA rules based on the analysis logic.
+*   **RAG Integration:** (Optional/If applicable) Retrieves context from a vector database of known malware families to improve classification accuracy.
+
+## ⚙️ How It Works
+
+1.  **Input:** User uploads a suspicious file (PE, ELF, Script, etc.) via the Gradio UI.
+2.  **Preprocessing:** The system extracts static features (hashes, imports, exports, entropy).
+3.  **Inference:**
+    *   The features are formatted into a prompt.
+    *   The LLM analyzes the features against known malware behaviors.
+4.  **Output:**
+    *   A JSON report detailed the findings.
+    *   A generated YARA rule to detect similar samples.
+
+## 🛠️ Installation & Usage
+
+You can try the live demo in the [Spaces tab](https://huggingface.co/spaces/YOUR_USERNAME/YOUR_SPACE_NAME).
+
+### Local Setup
+
+To run this tool locally, clone the repository and install the dependencies.
+
+```bash
+git clone https://huggingface.co/spaces/zeltera/mcma
+cd mcma
+pip install -r requirements.txt
+python app.py
+
+license: mit
+---