WaveVerif: Acoustic Side-Channel based Verification of Robotic Workflows
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866909877945761792 |
|---|---|
| author | Erdogan, Zeynep Yasemin Nagaraja, Shishir Ahmed, Chuadhry Mujeeb Shah, Ryan |
| author_facet | Erdogan, Zeynep Yasemin Nagaraja, Shishir Ahmed, Chuadhry Mujeeb Shah, Ryan |
| contents | In this paper, we present a framework that uses acoustic side-channel analysis (ASCA) to monitor and verify whether a robot correctly executes its intended commands. We develop and evaluate a machine-learning-based workflow verification system that uses acoustic emissions generated by robotic movements. The system can determine whether real-time behavior is consistent with expected commands. The evaluation takes into account movement speed, direction, and microphone distance. The results show that individual robot movements can be validated with over 80% accuracy under baseline conditions using four different classifiers: Support Vector Machine (SVM), Deep Neural Network (DNN), Recurrent Neural Network (RNN), and Convolutional Neural Network (CNN). Additionally, workflows such as pick-and-place and packing could be identified with similarly high confidence. Our findings demonstrate that acoustic signals can support real-time, low-cost, passive verification in sensitive robotic environments without requiring hardware modifications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_25960 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | WaveVerif: Acoustic Side-Channel based Verification of Robotic Workflows Erdogan, Zeynep Yasemin Nagaraja, Shishir Ahmed, Chuadhry Mujeeb Shah, Ryan Cryptography and Security Artificial Intelligence Robotics In this paper, we present a framework that uses acoustic side-channel analysis (ASCA) to monitor and verify whether a robot correctly executes its intended commands. We develop and evaluate a machine-learning-based workflow verification system that uses acoustic emissions generated by robotic movements. The system can determine whether real-time behavior is consistent with expected commands. The evaluation takes into account movement speed, direction, and microphone distance. The results show that individual robot movements can be validated with over 80% accuracy under baseline conditions using four different classifiers: Support Vector Machine (SVM), Deep Neural Network (DNN), Recurrent Neural Network (RNN), and Convolutional Neural Network (CNN). Additionally, workflows such as pick-and-place and packing could be identified with similarly high confidence. Our findings demonstrate that acoustic signals can support real-time, low-cost, passive verification in sensitive robotic environments without requiring hardware modifications. |
| title | WaveVerif: Acoustic Side-Channel based Verification of Robotic Workflows |
| topic | Cryptography and Security Artificial Intelligence Robotics |
| url | https://arxiv.org/abs/2510.25960 |