This project aims to enhance the performance of threat detection in Internet of Things (IoT) environments using intelligent approaches. With the rapid growth of IoT devices, ensuring robust security measures has become imperative. My solution leverages advanced machine learning techniques and data analysis to detect and mitigate threats efficiently.
- Anomaly Detection: Implements anomaly detection techniques to recognize unusual patterns that may indicate security breaches.
- Scalability: Designed to handle a large number of IoT devices with minimal performance degradation.
- Customizable Alerts: Provides customizable alerting mechanisms for different types of threats.
- Visualization: Offers comprehensive dashboards to visualize threat detection metrics and device status.
| Component 1-n Diagram |
|---|
| Chosen System Design |
- Programming Languages:
Python - Machine Learning Frameworks:
TensorFlow,Scikit-learn - Data Analysis: Pandas,
NumPy - Visualization:
Grafana,Matplotlib - Networking:
MQTT,HTTP,CoAPetc - Database:
MongoDB,SQLite
To install and run this project, follow these steps:
-
Clone the repository:
git clone https://github.com/ns7523/Threat-Detection-in-IoT.git cd Threat-Detection-in-IoT -
Run the project:
python app.py
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Data Collection:
- Predetermined and Trained Datasets.
- Tested and trained on 4-8 Lakhs of possibilites/datasets.
- The application will analyze the data and predict the attack possibilites.
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Homepage:
- Access at
LocalHostto predict threat detection and attack status.
- Access at
- Attack :
1 - No Attack :
0 - Accuracy:
90% - Precision:
90% - Recall:
90%
| Homepage | Sign Up |
|---|---|
| Prediction | Results |
For any inquiries or feedback, please contact me at nsakash752003@gmail.com
I hope this project helps in securing IoT environments more effectively. Happy coding!