docs: fix clone URL, Next.js badge v15, API description, add Demo section

Author: DHANUSHGCODE

README.md

@@ -2,7 +2,7 @@
 
 ![Build Status](https://img.shields.io/badge/build-passing-brightgreen)
 ![Python](https://img.shields.io/badge/Python-3.9%2B-blue)
-![Frontend](https://img.shields.io/badge/Next.js-13.0%2B-black)
+![Frontend](https://img.shields.io/badge/Next.js-15.0%2B-black)
 ![License](https://img.shields.io/badge/license-MIT-green)
 
 A comprehensive, **GSOC-level security monitoring solution** that leverages Artificial Intelligence to detect network anomalies, visualize threats in real-time, and provide actionable insights for system administrators.
@@ -21,36 +21,36 @@ flowchart LR
     Ingest --> DB[(SQLite via SQLAlchemy)]
     Ingest --> Model[Isolation Forest Anomaly Model]
     Model --> Scored[Scored Events - Normal / Suspicious / Critical]
-    Scored --> API[REST API]
-    API --> Dashboard[Next.js Dashboard]
-    Dashboard --> Visuals[3D Globe & Network Graph]
+    Scored --> Dashboard[Next.js Real-time Dashboard]
+    Dashboard --> Globe[3D Threat Globe]
+    Dashboard --> Graph[3D Network Topology]
+    Dashboard --> Charts[Recharts Visualizations]
 ```
 
 The platform ingests raw system and network logs, stores them in a relational database, scores them with an Isolation Forest model, and serves the results to a Next.js dashboard that renders traditional charts and immersive 3D visualizations.
 
 ## 🚀 Key Features
 
-*   **🕵️ Real-time Anomaly Detection**: Utilizes Unsupervised Learning (Isolation Forest) to detect outliers in network traffic and user behavior.
-*   **📊 Interactive Dashboard**: Built with **Next.js** and **Recharts** to visualize traffic spikes, threat distribution, and live logs.
-* **🌐 3D Threat Globe Visualization**: Interactive 3D globe using React Three Fiber to visualize global threat distribution with color-coded severity indicators.
-* **🔗 3D Network Topology Graph**: Real-time 3D network relationship mapping with interactive nodes showing server, client, and threat connections.
-* **✨ Advanced 3D Animations**: Smooth animations, hover effects, and auto-rotation for immersive threat monitoring experience.
-*   **⚡ High-Performance Backend**: **FastAPI** drives the API, ensuring millisecond-latency responses for log ingestion and inference.
-*   **📥 Universal Log Ingestion**: capable of parsing standardized system logs and network traffic data.
-*   **🚨 Threat Intelligence**: Automatically classifies events as "Normal", "Suspicious", or "Critical" based on AI confidence scores.
-
----
+- 🕵️ **Real-time Anomaly Detection**: Utilizes Unsupervised Learning (Isolation Forest) to detect outliers in network traffic and user behavior.
+- 📊 **Interactive Dashboard**: Built with **Next.js** and **Recharts** to visualize traffic spikes, threat distribution, and live logs.
+- 🌐 **3D Threat Globe Visualization**: Interactive 3D globe using React Three Fiber to visualize global threat distribution with color-coded severity indicators.
+- 🔗 **3D Network Topology Graph**: Real-time 3D network relationship mapping with interactive nodes showing server, client, and threat connections.
+- ✨ **Advanced 3D Animations**: Smooth animations, hover effects, and auto-rotation for immersive threat monitoring experience.
+- ⚡ **High-Performance Backend**: **FastAPI** drives the API, ensuring millisecond-latency responses for log ingestion and inference.
+- 📥 **Universal Log Ingestion**: Capable of parsing standardized system logs and network traffic data.
+- 🚨 **Threat Intelligence**: Automatically classifies events as "Normal", "Suspicious", or "Critical" based on AI confidence scores.
 
 ## 🛠️ Tech Stack
 
 ### Backend & AI
-*   **Python 3.10+**: Core logic.
-*   **FastAPI**: High-performance Async API framework.
-*   **Scikit-learn**: Machine Learning (Isolation Forest).
-*   **Pandas & NumPy**: Data manipulation and feature extraction.
-*   **SQLAlchemy & SQLite**: ORM and Database (Easily scalable to PostgreSQL).
 
-*   ### 🤖 Why Isolation Forest?
+- **Python 3.10+**: Core logic.
+- **FastAPI**: High-performance Async API framework.
+- **Scikit-learn**: Machine Learning (Isolation Forest).
+- **Pandas & NumPy**: Data manipulation and feature extraction.
+- **SQLAlchemy & SQLite**: ORM and Database (Easily scalable to PostgreSQL).
+
+### 🤖 Why Isolation Forest?
 
 This project focuses on unsupervised anomaly detection, where labeled attack data is rare and patterns evolve quickly. Isolation Forest is well-suited because:
 
@@ -62,32 +62,44 @@ This project focuses on unsupervised anomaly detection, where labeled attack dat
 These properties make Isolation Forest a practical baseline model for an AI-powered security monitoring platform, while leaving room for future extensions (autoencoders, ensembles, or hybrid rules-plus-ML systems).
 
 ### Frontend
-*   **Next.js 14**: React framework for production.
-*   **TailwindCSS**: Utility-first styling.
-*   **Recharts**: Composable charting library.
-*   **Lucide React**: Beautiful icons.
-* **Three.js**: WebGL 3D graphics library for immersive visualizations.
-* **React Three Fiber**: React renderer for Three.js enabling declarative 3D scenes.
-* **@react-three/drei**: Useful helpers and abstractions for React Three Fiber.
 
----
+- **Next.js 15**: React framework for production.
+- **TailwindCSS**: Utility-first styling.
+- **Recharts**: Composable charting library.
+- **Lucide React**: Beautiful icons.
+- **Three.js**: WebGL 3D graphics library for immersive visualizations.
+- **React Three Fiber**: React renderer for Three.js enabling declarative 3D scenes.
+- **@react-three/drei**: Useful helpers and abstractions for React Three Fiber.
+
+## 🖥️ Demo & Screenshots
+
+> **Live Demo**: Coming soon — Cloud deployment in progress (AWS/Render).
+
+| Dashboard View | 3D Threat Globe | Network Topology |
+|---|---|---|
+| ![Dashboard](docs/screenshots/dashboard.png) | ![Globe](docs/screenshots/globe.png) | ![Topology](docs/screenshots/topology.png) |
+
+> 📌 *Add screenshots to `docs/screenshots/` and update the paths above.*
 
 ## ⚡ Getting Started
 
 Follow these steps to set up the project locally.
 
 ### Prerequisites
-*   Python 3.9 or higher
-*   Node.js 18.0 or higher
-*   Git
+
+- Python 3.9 or higher
+- Node.js 18.0 or higher
+- Git
 
 ### 1. Clone the Repository
+
 ```bash
-git clone https://github.com/DHANUSHGCODE/ai-security-monitor.git
-cd ai-security-monitor
+git clone https://github.com/DHANUSHGCODE/AI-Powered-Security-Monitoring-Threat-Detection-Platform.git
+cd AI-Powered-Security-Monitoring-Threat-Detection-Platform
 ```
 
 ### 2. Backend Setup
+
 Initialize the Python environment and install dependencies.
 
 ```bash
@@ -102,64 +114,68 @@ pip install -r requirements.txt
 ```
 
 ### 3. Initialize AI Model
+
 Train the initial model using the provided simulation data.
 
 ```bash
 # From the root directory
 python ai-model/train_model.py
 ```
-*This will generate a `generated_logs.csv` dataset and save the trained model to `ai-model/isolation_forest_model.pkl`.*
+
+This will generate a `generated_logs.csv` dataset and save the trained model to `ai-model/isolation_forest_model.pkl`.
 
 ### 4. Start the Backend Server
+
 ```bash
 # From the root directory
 uvicorn backend.main:app --reload --port 8000
 ```
+
 The API will be available at `http://localhost:8000`.  
-interactive Docs: `http://localhost:8000/docs`.
+Interactive Docs: `http://localhost:8000/docs`.
 
 ### 5. Frontend Setup
+
 Open a new terminal and set up the Next.js dashboard.
 
 ```bash
 cd frontend
 npm install
 npm run dev
 ```
-The dashboard will launch at `http://localhost:3000`.
 
----
+The dashboard will launch at `http://localhost:3000`.
 
 ## 📊 API Documentation
 
 | Method | Endpoint | Description |
-| :--- | :--- | :--- |
+|--------|----------|-------------|
 | `GET` | `/` | Health check. |
 | `POST` | `/logs/` | Ingest a new log entry. |
 | `GET` | `/logs/` | Retrieve recent logs (supports pagination). |
-| `POST` | `/predict/` | Get capability score for a specific traffic pattern. |
-
----
+| `POST` | `/predict/` | Get anomaly score for a specific traffic pattern. |
 
 ## 🔮 Roadmap
 
 - [x] Core Log Ingestion & Database
 - [x] Basic Anomaly Detection Model
 - [x] Real-time Dashboard
+- [x] Docker Containerization
 - [ ] WebSocket Integration for Live Alerts
 - [ ] User Authentication (OAuth2)
-- [x] Docker Containerization
 - [ ] Deployment to Cloud (AWS/Render)
 
 ## 🤝 Contributing
 
 Contributions are welcome! Please fork the repository and submit a pull request for any enhancements.
 
-1.  Fork the Project
-2.  Create your Feature Branch (`git checkout -b feature/AmazingFeature`)
-3.  Commit your Changes (`git commit -m 'Add some AmazingFeature'`)
-4.  Push to the Branch (`git push origin feature/AmazingFeature`)
-5.  Open a Pull Request
+1. Fork the Project
+2. Create your Feature Branch (`git checkout -b feature/AmazingFeature`)
+3. Commit your Changes (`git commit -m 'Add some AmazingFeature'`)
+4. Push to the Branch (`git push origin feature/AmazingFeature`)
+5. Open a Pull Request
+
+See [CONTRIBUTING.md](CONTRIBUTING.md) for detailed guidelines.
 
 ## 📄 License