Research Project
Context-Aware Behavioral Fingerprinting for IoT Devices
Description
- Collected & analyzed 600K+ packets (Wireshark); engineered 18 packet-level features (e.g., TCP window size, payload entropy).
- Achieved ~94% accuracy via Random Forest for device identification in variable contexts.
- Deployed an IoT testbed (Raspberry Pi gateway + 8 NodeMCU nodes), lowering per-device cost by ~90% vs prior setups.
Stack: Python, C, Raspberry Pi, NodeMCU, scikit-learn, Wireshark
