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.