Elhanafy, Marim; Ravva, Srivaths; Solanki, Abhijeet; Hasan, Syed Rafay. “Towards Machine Learning Based Fingerprinting of Ultrasonic Sensors.” Conference Proceedings – IEEE SoutheastCon (2025): 1332–1333. https://doi.org/10.1109/SoutheastCon56624.2025.10971545.
“Fingerprinting” is a method used to identify devices based on their unique data patterns—kind of like how human fingerprints are used to tell people apart. This paper focuses on sensor fingerprinting, which means identifying individual sensors by the tiny, unique errors they have due to small imperfections from the manufacturing process.
The researchers created a model that uses these small error patterns to recognize specific sensors. They tested it using different machine learning algorithms, including a random forest classifier, multilayer perceptron, and soft decision tree. These techniques were able to correctly identify sensors with high accuracy—87%, 85.5%, and 89.2%, respectively.
These findings show that sensor fingerprinting is a promising and reliable way to identify or track sensors, which could be useful in areas like security, quality control, and device management.
Fig. 1:
High-level architecture of the testbed.