README.md

---
title: AI NIDS Student Project
emoji: 🛡️
colorFrom: blue
colorTo: green
sdk: streamlit
sdk_version: 1.39.0
app_file: app.py
pinned: false
---

# 🛡️ AI-Based Network Intrusion Detection System (Student Project)

This project demonstrates how to use **Machine Learning (Random Forest)** and **Generative AI (Grok)** to detect and explain network attacks (specifically DDoS).

## 🚀 How to Use
1. **Enter API Key:** Paste your Grok API key in the sidebar (optional, for AI explanations).
2. **Train Model:** Click the "Train AI Model" button. The system loads the `Friday-WorkingHours...` dataset automatically.
3. **Simulate:** Click "Simulate Random Packet" to pick a real network packet from the test set.
4. **Analyze:** See if the model flags it as **BENIGN** or **DDoS**, and ask Grok to explain why.

## 📂 Files
- `app.py`: The main Python application code.
- `requirements.txt`: List of libraries used.
- `Friday-WorkingHours-Afternoon-DDos.pcap_ISCX.csv`: The dataset (CIC-IDS2017 subset).

## 🎓 About
Created for a university cybersecurity project to demonstrate the integration of traditional ML and LLMs in security operations.