Physical Fingerprinting of Ultrasonic Sensors and Applications to Sensor Security
Abstract
As the market for autonomous vehicles advances, a need for robust safety protocols also increases. Autonomous vehicles rely on sensors to understand their operating environment. Active sensors such as camera, LiDAR, ultrasonic, and radar are vulnerable to physical channel attacks. One way to counter these attacks is to pattern match the sensor data with its own unique physical distortions, commonly referred to as a fingerprint. This fingerprint exists because of how the sensor was manufactured, and it can be used to determine the transmitting sensor from the received waveform. In this paper, using an ultrasonic sensor, we establish that there exists a specific distortion profile in the transmitted waveform called physical fingerprint that can be attributed to their intrinsic characteristics. We propose a joint time-frequency analysis-based framework for ultrasonic sensor fingerprint extraction and use it as a feature to train a Naive Bayes classifier. The trained model is used for transmitter identification from the received physical waveform.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Materials with high sonic wave absorption coefficients and damping capabilities, such as acoustic foam, might also affect ultrasonic sensor readings.
“Materials with high sonic wave absorption coefficients and damping capabilities, such as acoustic foam, might also affect ultrasonic sensor readings [93].”
From Sensor-Based Prognostic Health Management of Advanced Driver Assistance System for Autonomous Vehicles: A Recent Survey (2022) · cites this paper as [93]
Cited by
Sorted by citations
Raouf, I., Khan, A., Khalid, S., Sohail, M., Azad, M. M., & Kim, H. S. (2022) · Mathematics · doi:10.3390/math10183233 · Cited by 41 · see citing works
Sensors fingerprints using Machine Learning: a case study on dam monitoring systems
Assumpcao, P., de Oliveira, C. G., Melo, W. S., et al. (2022) · IEEE International Instrumentation and Measurement Technology Conference · doi:10.1109/i2mtc48687.2022.9806620 · Cited by 4 · see citing works
“In many cases, these sensing technologies can constitute monitoring systems that aim to detect abnormal behaviors to prevent or minimize their impacts [5], [9].”
BackgroundInfluentialA Secure Cloud-based Architecture for monitoring Cyber-Physical Critical Infrastructures
Assumpcao, P., de Oliveira, C. G., Ortiz, P., et al. (2022) · Cyber Security in Networking Conference (CSNet) · doi:10.1109/csnet56116.2022.9955607 · Cited by 2 · see citing works
“They can constitute monitoring solutions for cyber-physical infrastructures, aiming to detect abnormal behavior to prevent or minimize its impacts [1], [2].”
BackgroundNuance: Near Ultrasound Attack on Networked Communication Environments
McKee, F., & Noever, D. A. (2023) · International Journal on Cybernetics & Informatics · doi:10.5121/ijci.2023.120307 · Cited by 1 · see citing works
“One approach involves using the physical fingerprinting of ultrasonic sensors to enhance their security [68], while another focuses on canceling inaudible voice commands against voice control systems [8].”
BackgroundNEAR ULTRASONIC ATTACK AND DEFENSIVE COUNTERMEASURES
McKee, F., & Noever, D. A. (2023) · International Journal of Network Security & Its Applications · doi:10.5121/ijnsa.2023.15301
“One approach involves using the physical fingerprinting of ultrasonic sensors to enhance their security [68], while another defense focuses on canceling inaudible voice commands against voice control systems [8].”
BackgroundTowards Machine Learning Based Fingerprinting of Ultrasonic Sensors
Elhanafy, M., Ravva, S., Solanki, A., et al. (2025) · Proceedings of IEEE Southeastcon · doi:10.1109/southeastcon56624.2025.10971545
“This approach is particularly useful for sensors, such as ultrasonic sensors extensively employed in the manufacturing, automotive, and medical sectors [8].”
- Authors
- Cheek, E., Khuttan, D., Changalvala, R., & Malik, H.
- Affiliations
- University of Michigan–Dearborn (US)
- Venue
- 2020 IEEE 6th International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application (DependSys)
- Publisher
- IEEE
- Published
- 2020-12
- Pages
- 65-72
- OpenAlex
- W3129833723
- Cited by
- 7
- References
- 0
- Citations by year
- 2025: 1 · 2023: 3 · 2022: 3
- FWCI
- 0.57
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications (DependSys) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Wireless Signal Modulation Classification
- Physical Sciences › Computer Science › Hardware and Architecture › Physical Unclonable Functions (PUFs) and Hardware Security
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Wireless Communication Security Techniques
- Keywords
- ultrasonic sensors, sensor security, transmitter identification, autonomous vehicles, waveform distortion, time-frequency analysis, Naive Bayes classifier, sensor spoofing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
