Update README.md

README.md

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 ---
+
 license: apache-2.0
 datasets:
 - SilverDragon9/UNSW_TON-IoT_Train_Test_IoT_Datasets
@@ -16,4 +17,48 @@ tags:
 - Intrusion
 - Detection
 - IDS
+
+---
+
+---
+## 🌐 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. 
+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
+json
+Copy
+Edit
+{
+  "date": "2025-04-11",
+  "time": "14:35:22",
+  "prediction": "Scanning"
+}
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