Drone Authentication via Acoustic Fingerprint

Diao, Y., Zhang, Y., Zhao, G. and Khamis, M. (2022) Drone Authentication via Acoustic Fingerprint. In: Annual Computer Security Applications Conference (ACSAC 2022), Austin, TX, USA, 5-9 Dec 2022, pp. 658-668. ISBN 9781450397599 (doi: 10.1145/3564625.3564653)

Abstract

As drones become widely used in different applications, drone authentication becomes increasingly important due to various security risks, e.g., drone impersonation attacks. In this paper, we propose an idea of drone authentication based on Mel-frequency cepstral coefficient (MFCC) using an acoustic fingerprint that is physically embedded in each drone. We also point out that the uniqueness of the drone’s sound comes from the combination of bodies (motors) and propellers. In the experiment with 8 drones, we compare the authentication accuracy of different feature extraction settings. Three kinds of different sound features are used: MFCC, delta MFCC (DMFCC), and delta-delta MFCC (DDMFCC). We choose the feature extraction settings and the sound features according to the best authentication result. In the experiment with 24 drones, we compare the closed set authentication performance of eight machine learning methods in terms of recall under the influence of additive white Gaussian noise (AWGN) with different levels of signal-to-noise ratio (SNR). Furthermore, we conduct an open set drone authentication experiment. Our results show that Quadratic Discriminant Analysis (QDA) outperforms other methods in terms of the highest average recall (94.19%) in the authentication of registered drones and the third highest average recall (82.35%) in the authentication of unregistered drones.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Zhao, Dr Guodong and Diao, Yufeng and Khamis, Dr Mohamed
• Authors: Diao, Y., Zhang, Y., Zhao, G., and Khamis, M.
• College/School: College of Science and Engineering; College of Science and Engineering > School of Computing Science; College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity
• ISBN: 9781450397599
• Published Online: 05 December 2022
• Copyright Holders: © 2022 Copyright held by the owner/author(s)
• First Published: First published in ACSAC '22: Proceedings of the 38th Annual Computer Security Applications Conference: 658-668
• Publisher Policy: Reproduced in accordance with the publisher copyright policy

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