🛡️ BoT-IoT Attack Detection & IP Analysis Dashboard

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📌 Overview

This project is a Machine Learning-based Network Intrusion Detection System built using the BoT-IoT dataset.

It predicts whether network traffic is:

• ✅ Normal (0)
• 🚨 Attack (1)

The results are displayed using an interactive Streamlit dashboard, which also highlights unique attacker IP addresses for deeper analysis.

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✨ Features

• 🔍 ML-based attack detection
• 📊 Interactive Streamlit dashboard
• 🚨 Filter traffic where attack_prediction = 1
• 🌐 Extract and display unique attacker IPs
• 📈 Attack distribution visualization

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🧠 Tech Stack

• Python 🐍
• Pandas
• Scikit-learn
• Streamlit
• NumPy

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📂 Project Structure

project_root/
│
├── app.py             # Main Streamlit application (UI + prediction logic)
├── model.pkl          # Trained machine learning model
├── scaler.pkl         # Data scaler for preprocessing
├── requirements.txt   # Project dependencies

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⚙️ Installation

# Clone repository
git clone https://github.com/sanikayadav2024/iot_security_ui.git

# Navigate to project folder
cd iot_security_ui

# Install dependencies
pip install -r requirements.txt

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▶️ Usage

streamlit run app.py

Then open in your browser:

http://localhost:8501

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📈 Output

• 📋 Table of predictions
• 🌐 Unique attacker IP list
• 📊 Attack distribution chart

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📸 Screenshots (Add yours)

💡 Tip: Add screenshots here to make your project stand out on GitHub

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👨‍💻 Author

Sanika Yadav GitHub: https://github.com/sanikayadav2024

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⭐ Support

If you found this project useful, consider giving it a ⭐ on GitHub!