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---
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](https://research.unsw.edu.au/projects/toniot-datasets) |
## 🔍 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 |
--- |