A newer version of the Streamlit SDK is available:
1.55.0
metadata
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
- Enter API Key: Paste your Grok API key in the sidebar (optional, for AI explanations).
- Train Model: Click the "Train AI Model" button. The system loads the
Friday-WorkingHours...dataset automatically. - Simulate: Click "Simulate Random Packet" to pick a real network packet from the test set.
- 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.