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