IoMT Secure Dashboard

Python, Streamlit, Scikit-learn, Random Forest, SVM, FastAPI, Matplotlib, Joblib, Pandas

This project is a real-time anomaly detection dashboard built to monitor and detect cyber threats in Internet of Medical Things (IoMT) environments. By leveraging machine learning, it provides a vital layer of security for smart hospital systems.

Github Repo

Inspiration

As healthcare systems become increasingly connected, they also become more vulnerable to cyberattacks. I created this dashboard to provide healthcare IT professionals with a real-time tool to detect and visualize anomalies—such as spoofing, unauthorized access, and ransomware—before they can compromise patient safety or data.

What it does

The IoMT Anomaly Detection Dashboard offers a suite of tools for real-time monitoring and model evaluation:

• Real-time Simulation: Simulates IoMT data streams to provide live predictions on network health.
• Dual-Model Detection: Utilizes both Random Forest and Support Vector Machine (SVM) models to identify anomalies.
• Model Evaluation: Displays live confusion matrices to help researchers understand model performance.
• Automated Logging: Maintains a detection_log.csv of all predictions for post-incident analysis and performance auditing.

Demo

How I built it

• Data Source: IoMT.csv dataset containing labeled network traffic patterns.
• Machine Learning: Developed using Python and Scikit-learn, focusing on Random Forest and SVM classifiers for high-accuracy threat detection.
• Dashboard: Built with Streamlit to create an interactive, web-based UI that handles live data processing.
• Preprocessing: Utilized StandardScaler and Joblib for efficient model serialization and real-time feature scaling.

Architecture

Challenges I ran into

• Real-time Performance: Ensuring the dashboard could process and visualize data points rapidly without lag.
• Model Accuracy: Tuning the SVM model to minimize false positives, which are critical in a healthcare setting to avoid “alert fatigue.”
• Data Structuring: Handling the specific feature requirements of medical IoT devices while maintaining a clean preprocessing pipeline.

Accomplishments that I am proud of

• Successfully integrated two distinct ML models into a single, cohesive dashboard.
• Developed a functional logging system that records threats automatically.
• Created a tool that bridges the gap between complex machine learning research and practical cybersecurity application.

What I learned

• How to deploy machine learning models into a live Streamlit environment.
• The specific characteristics of IoMT network traffic and how they differ from standard IT environments.
• Advanced visualization techniques for displaying model evaluation metrics like confusion matrices in real-time.

What is next for IoMT Dashboard

• Integrate the FastAPI backend to support remote data ingestion from actual medical devices.
• Implement deep learning models to better detect time-series based attack patterns.
• Add an automated alert system that sends notifications via email or SMS when high-severity threats are detected.

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