A Python-based framework for high-frequency data collection and visualization from industrial IoT sensors. Developed to support cybersecurity research on side-channel attack analysis at George Mason University.
This tool interfaces with STWIN.box and STM32-Nucleo microcontrollers to:
- Record raw sensor data streams.
- Export datasets to CSV for offline analysis.
- Generate plots for multi-sensor data verification.
The framework allows for configuration of acquisition parameters (sampling rate, duration, sensor selection) via the comments/config files. It is designed to preprocess data for machine learning models (CNN/TFLite) used in edge security research (side-channel).
Cybersecurity Research Intern | GMU College of Engineering (2023): Developed to test side-channel attack prevention mechanisms on embedded IoT devices.