diff --git a/README.md b/README.md
new file mode 100644
index 0000000..60dcd55
--- /dev/null
+++ b/README.md
@@ -0,0 +1,107 @@
+# AntiVE-BehaviorWatch
+
+![Language](https://img.shields.io/badge/language-C-00599C?logo=c&logoColor=white)
+![Platform](https://img.shields.io/badge/platform-Windows-0078D6?logo=windows&logoColor=white)
+![AI](https://img.shields.io/badge/AI-GRU%20Neural%20Network-purple)
+![Inference](https://img.shields.io/badge/inference-Embedded%20AI-success)
+![Detection](https://img.shields.io/badge/detection-Behavioral%20Verification-orange)
+![Status](https://img.shields.io/badge/status-Active%20Research-red)
+
+<img width="1408" height="768" alt="AntiVE_BehaviorWatch" src="https://github.com/user-attachments/assets/09bc71d0-f1b3-4231-b9de-75c387484072" />
+
+
+> **Embedded AI-powered human behavior verification for cybersecurity research.**
+
+---
+
+# What is AntiVE-BehaviorWatch?
+
+**AntiVE-BehaviorWatch** is a Windows-based cybersecurity research project that explores a new approach to execution control using **embedded artificial intelligence**.
+
+Instead of relying on traditional anti-analysis techniques such as virtual machine detection, sandbox detection, debugger checks, or hardware fingerprinting, the project uses an embedded **GRU (Gated Recurrent Unit) neural network** to determine whether a **real human** is interacting with the system.
+
+The model analyzes real-time mouse behavior—including cursor movement, velocity, acceleration, jerk, angular velocity, idle patterns, and motion characteristics—to make an intelligent runtime decision before allowing the protected execution path to continue.
+
+---
+
+<img width="1919" height="1035" alt="AntiVE" src="https://github.com/user-attachments/assets/4e5fd10f-2241-4c3e-9847-f6f84ad23298" />
+
+---
+
+# Architecture
+
+  ```
+  Mouse Movement
+         │
+         ▼
+  Feature Extraction
+         │
+         ▼
+  Embedded GRU Neural Network
+         │
+         ▼
+  Behavior Classification
+         │
+   ┌─────┴────────┐
+   │              │
+   ▼              ▼
+  Human      Idle / Artificial
+  Verified      Behavior
+   │              │
+   ▼              ▼
+  Continue     Abort
+  Execution
+
+  ```
+
+---
+
+# Behavioral Features
+
+The embedded model analyzes multiple behavioral features:
+
+- Cursor Distance
+- Cursor Velocity
+- Acceleration
+- Jerk
+- Mouse Angle
+- X-axis Movement
+- Y-axis Movement
+- Idle State
+- Idle Streak Length
+
+These features are collected continuously over multiple observation phases before inference.
+
+---
+
+# Features
+
+- Embedded GRU neural network
+- Offline AI inference
+- No external AI runtime required
+- Real-time behavioral telemetry
+- Human interaction verification
+- Multi-phase analysis
+- Majority voting decision engine
+- Lightweight C implementation
+- Windows-native application
+- Embedded model weights
+- Runtime behavior classification
+
+---
+
+# Disclaimer
+
+This project is intended **strictly for cybersecurity research, machine learning experimentation, education, and authorized security testing**.
+
+It demonstrates how embedded AI can be used for behavioral verification and runtime decision-making. It is not intended for unauthorized or malicious use.
+
+---
+
+# Author
+
+**Nirvana (0xNirSec)**
+
+*"Trust behavior, not the environment."*
+
+