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README.md

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+---
+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.

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requirements.txt

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+streamlit
+pandas
+numpy
+scikit-learn
+groq