Website_URL: https://codes-32bit.lovable.app/
-
npm i
2. npm run dev
-
[localhost](http://localhost:3000/) Example
**Use Vs Code**
**Use your preferred IDE (Vs code is Recomended)**
If you want to run it locally
Follow these steps:
```sh
# Step 1: Clone the repository using the project's Git URL.
git clone <YOUR_GIT_URL>
# Step 2: Navigate to the project directory.
cd <YOUR_PROJECT_NAME>
# Step 3: Install the necessary dependencies.
npm i
# Step 4: Start the development server with auto-reloading and an instant preview.
npm run dev
This project is an AI-assisted cybersecurity simulation system designed to enhance both offensive (Red Team) and defensive (Blue Team) capabilities through realistic, data-driven scenarios. It provides a unified environment to analyze, simulate, and visualize cyber incidents dynamically.
Simulates real-world attack scenarios such as reconnaissance, phishing, and exploit testing. Helps identify potential vulnerabilities and assess how systems react under simulated threat conditions. Encourages ethical hacking practice to strengthen system security posture.
Detects, analyzes, and responds to simulated attacks using intelligent pattern recognition. Evaluates defense mechanisms and highlights areas requiring immediate attention. Assists teams in improving incident response time and accuracy.
Displays attack paths, detection timelines, and system responses in real time. Provides clear visual insights into how Red Team attacks evolve and how the Blue Team mitigates them. Includes threat metrics, log summaries, and response effectiveness scores to measure defense readiness. Enables post-simulation comparison between Red and Blue strategies for continuous learning.
After each simulation, a custom analytical report is automatically generated. The report includes attack vectors, detection outcomes, and performance summaries. Users can download the report to document and review their cybersecurity performance.
This project is built with:
- Vite
- TypeScript
- React
- shadcn-ui
- Tailwind CSS
- HTML
- CSS
codes-32bit-sadrita-neogi.mp4
https://sadrita-security-report.tiiny.site/
