---
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       |

---