Description: In this Project, we propose an IoT-based IDS that utilizes supervised data and the K-Nearest Neighbors (KNN) algorithm. Our system takes data packets captured from Wireshark, containing network traffic data, and predicts whether the traffic is normal or abnormal. It can also classify the type of attack, such as Mirai, DDoS, or other attacks.
ML Includes all the neccessary machine learning files. Static containes CSS, JS, Font files Templates contains, HTML code.
Prerequistive:
- Python
- Basic Html
- Basic Css
Installation:
- VSCode or any IDE.
- Mysql Workbench (prefer to install the application)
- Python Packages Install
- Mailtrap Account to get the API for the using of MAIL system.
- Jupyter Notebook
How to present or Run this project:
- Train the model or Load it from Pickle.
- Run the flask application with the running backend MYSQL.
- Open you mail on Mailtrap.io for the notification of anomaly detection. Make decision to block or ignore.
Future Scope: When Anomalies are detected, the source address should be saved in the firewall.
NEED Full DATASET? or Any Question? Feel free to contact: Please contact at fa19c2bb034@iub.edu.pk