# AudioUnlock: Device-to-Device Authentication via Acoustic Signatures and One-Class Classifiers

## TL;DR

This paper introduces AudioUnlock, a device authentication system using acoustic signatures and one-class classifiers to verify devices in environments like vehicles.

## Contribution

The novel approach uses one-class classification to authenticate a single device without needing data from other devices during training.

## Key findings

- AudioUnlock achieves recognition rates from 50% to 100% across various environmental conditions.
- The system is tested using smartphones and automotive-grade headunits with over 5000 measurements.

## Abstract

Acoustic fingerprints can be used for device-to-device authentication due to manufacturing-induced variations in microphones and speakers. However, previous works have focused mostly on recognizing single devices from a set of multiple devices, which may not be sufficiently realistic since in practice, a single device has to be recognized from a very large pool of devices that are not available for training machine learning classifiers. Therefore, in this work, we focus on one-class classification algorithms, namely one-class Support Vector Machine and the local outlier factor. As such, learning the fingerprint of a single device is sufficient to recognize the legitimate device and reject all other attempts to impersonate it. The proposed application can also rely on cloud-based deployment to free the smartphone from intensive computational tasks or data storage. For the experimental part, we rely both on smartphones and an automotive-grade Android headunit, exploring in-vehicle environments as the main area of application. We create a dataset consisting of more than 5000 measurements and achieve a recognition rate ranging from 50% to 100% for different devices under various environmental conditions such as distance, altitude, and component aging. These conditions also serve as our limitations, however, we propose different solutions for overcoming them, which are part of our threat model.