metadata
license: apache-2.0
datasets:
- SilverDragon9/UNSW_TON-IoT_Train_Test_IoT_Datasets
- SilverDragon9/UNSW_TON-IoT_Train_Test_OS_Datasets
language:
- en
- el
metrics:
- accuracy
- f1
- precision
tags:
- IOT
- CyberSecurity
- Intrusion
- Detection
- IDS
library_name: sklearn
🌐 Overview
Sniffer.AI is an AI-powered Intrusion Detection System (IDS) for IoT networks, designed to detect and classify suspicious behavior across smart devices in real-time. /n
Built on ensemble machine learning models trained on the UNSW TON_IoT dataset, it classifies activity into Normal or one of 7 attack types.
📡 Target Devices: Fridge, GPS Tracker, Garage Door, Thermostat, Weather Station
📁 Output can be saved for offline analysis and archiving
📦 Key Features
| Feature | Description |
|---|---|
| 🧠 Ensemble Models | RF, XGBoost, AdaBoost, Bagging, Decision Trees |
| 🧪 Predicts Threat Category | Normal vs 7 Attack Types |
| 🕒 Timestamps Every Detection | Provides real-time date & time in output |
| 💾 Downloadable Results | Output can be saved as .csv or .json |
| 🌐 Edge Ready | Lightweight enough for IoT Gateway deployment |
| 📚 Dataset Used | UNSW TON_IoT |
🔍 Attack Categories
- text
- Normal
- Backdoor
- DDoS
- Injection
- Password Attack
- Ransomware
- Scanning
- XSS
📊 Sample Output Format
| Date | Time | Prediction |
|---|---|---|
| 2025-04-11 | 14:35:22 | Scanning |