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).

Live Project Link:

https://huggingface.co/spaces/subhadeepghosh/SecureNet

Dataset Link:

(https://huggingface.co/spaces/subhadeepghosh/SecureNet/blob/main/Friday-WorkingHours-Afternoon-DDos.pcap_ISCX.csv)

🚀 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.