research-article

DronePrint: Acoustic Signatures for Open-set Drone Detection and Identification with Online Data

Authors:

Harini Kolamunna,

Thilini Dahanayaka,

Suranga Seneviratne,

Kanchana Thilakaratne,

Albert Y. Zomaya,

Aruna SeneviratneAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 5, Issue 1

Article No.: 20, Pages 1 - 31

https://doi.org/10.1145/3448115

Published: 30 March 2021 Publication History

Abstract

With the ubiquitous availability of drones, they are adopted benignly in multiple applications such as cinematography, surveying, and legal goods delivery. Nonetheless, they are also being used for reconnaissance, invading personal or secure spaces, harming targeted individuals, smuggling drugs and contraband, or creating public disturbances. These malicious or improper use of drones can pose significant privacy and security threats in both civilian and military settings. Therefore, it is vital to identify drones in different environments to assist the decisions on whether or not to contain unknown drones. While there are several methods proposed for detecting the presence of a drone, they have limitations when it comes to low visibility, limited access, or hostile environments. In this paper, we propose DronePrint that uses drone acoustic signatures to detect the presence of a drone and identify the make and the model of the drone. We address the shortage of drone acoustic data by relying on audio components of online videos. In drone detection, we achieved 96% accuracy in a closed-set scenario, and 86% accuracy in a more challenging open-set scenario. Our proposed method of cascaded drone identification, where a drone is identified for its 'make' followed by the 'model' of the drone achieves 90% overall accuracy. In this work, we cover 13 commonly used commercial and consumer drone models, which is to the best of understanding is the most comprehensive such study to date. Finally, we demonstrate the robustness of DronePrint to drone hardware modifications, Doppler effect, varying SNR conditions, and in realistic open-set acoustic scenes.

References

[1]

Sara Al-emadi, Abdulla Al-ali, Amr Mohammad, and Abdulaziz Al-ali. 2019. Audio Based Drone Detection and Identification using Deep Learning. In IWCMC'19. IEEE, 459--464.

[2]

Riham Altawy and Amr M Youssef. 2016. Security, privacy, and safety aspects of civilian drones: A survey. ACM Transactions on Cyber-Physical Systems 1, 2 (2016), 1--25.

Digital Library

[3]

Muhammad Zohaib Anwar, Zeeshan Kaleem, and Abbas Jamalipour. 2019. Machine Learning Inspired Sound-Based Amateur Drone Detection for Public Safety Applications. IEEE Transactions on Vehicular Technology 68, 3 (2019), 2526--2534.

[4]

S Batra. 2018. 41 Years (1978-2018) JEE Advanced (IIT-JEE) + 17 yrs (2002-2018). Disha.

[5]

Abhijit Bendale and Terrance E Boult. [n.d.]. Towards Open Set Deep Networks 1 Introduction. ([n. d.]).

[6]

Terrance E Boult, Steve Cruz, Akshay Raj Dhamija, M Gunther, James Henrydoss, and Walter J Scheirer. 2019. Learning and the unknown: Surveying steps toward open world recognition. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 9801--9807.

Digital Library

[7]

Aldrich A Cabrera-Ponce, J Martinez-Carranza, and Caleb Rascon. 2020. Detection of nearby UAVs using a multi-microphone array on board a UAV. International Journal of Micro Air Vehicles 12 (2020), 1756829320925748. https://doi.org/10.1177/1756829320925748 arXiv:https://doi.org/10.1177/1756829320925748

[8]

Gürol Canbek, Seref Sagiroglu, Tugba Taskaya Temizel, and Nazife Baykal. 2017. Binary Classification Performance Measures/Metrics: A comprehensive visualized roadmap to gain new insights. 821--826. https://doi.org/10.1109/UBMK.2017.8093539

[9]

Victoria Chang, Pramod Chundury, and Marshini Chetty. 2017. Spiders in the Sky: User Perceptions of Drones, Privacy, and Security. 6765--6776. https://doi.org/10.1145/3025453.3025632

[10]

Jagmohan Chauhan, Yining Hu, Suranga Seneviratne, Archan Misra, Aruna Seneviratne, and Youngki Lee. 2017. BreathPrint: Breathing acoustics-based user authentication. In Proceedings of the 15th Annual International Conference on Mobile Systems, Applications, and Services. 278--291.

Digital Library

[11]

Jagmohan Chauhan, Jathushan Rajasegaran, Suranga Seneviratne, Archan Misra, Aruna Seneviratne, and Youngki Lee. 2018. Performance Characterization of Deep Learning Models for Breathing-Based Authentication on Resource-Constrained Devices. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 2, 4 (Dec. 2018).

Digital Library

[12]

Philip Church, Christopher Grebe, Justin Matheson, and Brett Owens. 2018. Aerial and surface security applications using lidar. In Laser Radar Technology and Applications XXIII, Monte D. Turner and Gary W. Kamerman (Eds.), Vol. 10636. International Society for Optics and Photonics, SPIE, 27--38. https://doi.org/10.1117/12.2304348

[13]

Google Cloud. 2020. Advanced Guide to Inception v3 on Cloud TPU. https://cloud.google.com/tpu/docs/inception-v3-advanced

[14]

Gael Fashingbauer Cooper. 2017. Watch a drone crash into Seattle's Space Needle. https://www.cnet.com/news/drone-crash-seattle-space-needle/

[15]

Akshay Raj Dhamija, G Manuel, and Terrance E Boult. 2018. Reducing Network Agnostophobia. NeurIPS (2018).

[16]

Eleni Diamantidou, Antonios Lalas, Konstantinos Votis, and Dimitrios Tzovaras. 2019. Multimodal Deep Learning Framework for Enhanced Accuracy of UAV Detection. In Computer Vision Systems, Dimitrios Tzovaras, Dimitrios Giakoumis, Markus Vincze, and Antonis Argyros (Eds.). Springer International Publishing, Cham, 768--777.

[17]

DJI. [n.d.]. INSPIRE 1Specs. https://www.dji.com/au/inspire-1/info

[18]

DJI. [n.d.]. Matrice 100. https://www.dji.com/au/matrice100

[19]

DJI. [n.d.]. Mavic Pro. https://www.dji.com/au/mavic

[20]

DJI. [n.d.]. Phantom 4 Pro. https://www.dji.com/au/phantom-4-pro

[21]

DJI. [n.d.]. Spark. https://www.dji.com/au/spark

[22]

DronePrint. [n.d.]. https://github.com/DronePrint/DronePrint.

[23]

Sphere Drones. [n.d.]. DJI Inspire 1 - 3510 Motor (CW). https://shop.spheredrones.com.au/products/dji-inspire-1-3510-motor-cw

[24]

Martins Ezuma, Faith Erden, Chethan Kumar Anjinappa, Ozgur Ozdemir, and Ismail Guvenc. 2019. Micro-UAV Detection and Classification from RF Fingerprints Using Machine Learning Techniques. In 2019 IEEE Aerospace Conference. 1--13.

[25]

Martin Ezuma, Faith Erden, Chethan Kumar Anjinappa, Ozgur Ozdemir, and Ismail Guvenc. 2020. Detection and Classification of UAVs Using RF Fingerprints in the Presence of Wi-Fi and Bluetooth Interference. IEEE Open Journal of the Communications Society 1 (2020), 60--76.

[26]

Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 (2014).

[27]

Alex Graves, Abdel rahman Mohamed, and Geoffrey Hinton. 2013. Speech recognition with deep recurrent neural networks. In 2013 IEEE International Conference on Acoustics, Speech and Signal Processing. 6645--6649.

[28]

François Grondin and François Michaud. 2019. Lightweight and optimized sound source localization and tracking methods for open and closed microphone array configurations. Robotics and Autonomous Systems 113 (2019), 63--80. https://doi.org/10.1016/j.robot.2019.01.002

Digital Library

[29]

Omar Adel Ibrahim, Savio Sciancalepore, and Roberto Di Pietro. 2020. Noise2Weight: On Detecting Payload Weight from Drones Acoustic Emissions. arXiv:2005.01347 [eess.AS]

[30]

ISCE. 2020. What's so sacrosanct about 10 dB Signal to noise ratio? https://www.isce.org.uk/articles/whats-so-sacrosanct-about-10-db-signal-to-noise-ratio/

[31]

Md Tamzeed Islam, Bashima Islam, and Shahriar Nirjon. 2017. SoundSifter: Mitigating Overhearing of Continuous Listening Devices. In Proceedings of the 15th Annual International Conference on Mobile Systems, Applications, and Services (MobiSys '17). Association for Computing Machinery, 29--41.

Digital Library

[32]

Raya Islam and Dr. Alexander Stimpson. 2017. Small UAV Noise Analysis.

[33]

navdeep jaitly and e geoffrey hinton. 2013. Vocal Tract Length Perturbation (VTLP) improves speech recognition. (2013).

[34]

Sungho Jeon, Jong-Woo Shin, Young-Jun Lee, Woong-Hee Kim, YoungHyoun Kwon, and Hae-Yong Yang. 2017. Empirical study of drone sound detection in real-life environment with deep neural networks. In 2017 25th European Signal Processing Conference (EUSIPCO). 1858--1862.

[35]

Therese Jones. 2017. International commercial drone regulation and drone delivery services. Technical Report. RAND.

[36]

Byungkwan Kim, Hyunseong Kang, and Seong-Ook Park. 2017. Drone Classification Using Convolutional Neural Networks With Merged Doppler Images. IEEE Geoscience and Remote Sensing Letters 14 (2017), 38--42.

[37]

Nicola Kloet, Simon Watkins, and Reece Clothier. 2017. Acoustic signature measurement of small multi-rotor unmanned aircraft systems. International Journal of Micro Air Vehicles 9, 1 (2017), 3--14.

[38]

Harini Kolamunna, Junye Li, Thilini Dahanayaka, Suranga Seneviratne, Kanchana Thilakaratne, Albert Y. Zomaya, and Aruna Seneviratne. 2020. POSTER: AcousticPrint: Acoustic Signature based Open Set Drone Identification. https://wisec2020.ins.jku.at/proceedings/wisec20-5.pdf.

[39]

J. Lauzon, F. Grondin, D. Létourneau, A. L. Desbiens, and F. Michaud. 2017. Localization of RW-UAVs using particle filtering over distributed microphone arrays. In 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 2479--2484.

[40]

C. Lazaro, J.P. Marques, G. Marchesan, and G. Cardoso. 2018. Waveform asymmetry of instantaneous current signal based symmetrical fault detection during power swing. Electric Power Systems Research 155 (2018), 340--349.

[41]

Dongkyu Rroyr Lee, Woong Gyu La, and Hwangnam Kim. 2018. Drone Detection and Identification System using Artificial Intelligence. In 9th International Conference on Information and Communication Technology Convergence. Institute of Electrical and Electronics Engineers Inc., 1131--1133. https://doi.org/10.1109/ICTC.2018.8539442 9th International Conference on Information and Communication Technology Convergence, ICTC 2018; Conference date: 17-10-2018 Through 19-10-2018.

[42]

Bernhard Lehner, Khaled Koutini, Christopher Schwarzlmüller, Thomas Gallien, and Gerhard Widmer. 2019. Acoustic scene classification with reject option based on resnets. In Proceedings of the Detection and Classification of Acoustic Scenes and Events Workshop (DCASE), New York, NY, USA. 25--26.

[43]

Natasha Lomas. 2018. Analysis backs claim drones were used to attack Venezuela's president. https://techcrunch.com/2018/08/08/analysis-backs-claim-drones-were-used-to-attack-venezuelas-president/

[44]

Georgia Lykou, Dimitrios Moustakas, and Dimitris Gritzalis. 2020. Defending Airports from UAS: A Survey on Cyber-Attacks and Counter-Drone Sensing Technologies. Sensors 20, 12 (2020), 3537. https://doi.org/10.3390/s20123537

[45]

Abdirahman Mohamud and Ashwin Ashok. 2018. Drone Noise Reduction through Audio Waveguiding. In Proceedings of the 4th ACM Workshop on Micro Aerial Vehicle Networks, Systems, and Applications (Munich, Germany) (DroNet'18). Association for Computing Machinery, New York, NY, USA, 92--94. https://doi.org/10.1145/3213526.3213543

Digital Library

[46]

Pavlo Molchanov, Karen O. Egiazarian, Jaakko Astola, R. Harmanny, and Jos De Wit. 2013. Classification of small UAVs and birds by micro-Doppler signatures. 2013 European Radar Conference (2013), 172--175.

[47]

M. Y. Mustafa, G. Polanco, Q. Gao, Y. Xu, A. Mustafa, and Q. Z. Al-Hamdan. 2014. Application of microphone arrays for the detection of acoustic noise in porous panel shields. In 2014 5th IEEE Conference on Cognitive Infocommunications (CogInfoCom). 549--553.

[48]

F. Nesta and M. Omologo. 2012. Generalized State Coherence Transform for Multidimensional TDOA Estimation of Multiple Sources. IEEE Transactions on Audio, Speech, and Language Processing 20, 1 (2012), 246--260.

Digital Library

[49]

Natalia Neverova, Christian Wolf, Griffin Lacey, Lex Fridman, Deepak Chandra, Brandon Barbello, and Graham Taylor. 2016. Learning human identity from motion patterns. IEEE Access 4 (2016), 1810--1820.

[50]

BBC News. [n.d.]. Drug delivery drone crashes in Mexico. https://www.bbc.com/news/technology-30932395.

[51]

BBC News. [n.d.]. Gatwick airport: How can a drone cause so much chaos? https://www.bbc.com/news/technology-46632892.

[52]

BBC News. [n.d.]. Heathrow airport: Drone sighting halts departures. https://www.bbc.com/news/uk-46803713.

[53]

Phuc Nguyen, Hoang Truong, Mahesh Ravindranathan, Anh Nguyen, Richard Han, and Tam Vu. 2017. Matthan: Drone Presence Detection by Identifying Physical Signatures in the Drone's RF Communication. In MobiSys '17. 211--224.

Digital Library

[54]

Nicolas Papernot, Patrick McDaniel, Xi Wu, Somesh Jha, and Ananthram Swami. 2016. Distillation as a defense to adversarial perturbations against deep neural networks. In 2016 IEEE Symposium on Security and Privacy (SP). IEEE, 582--597.

[55]

Daniel S Park, William Chan, Yu Zhang, Chung-Cheng Chiu, Barret Zoph, Ekin D Cubuk, and Quoc V Le. 2019. Specaugment: A simple data augmentation method for automatic speech recognition. arXiv preprint arXiv:1904.08779 (2019).

[56]

Parrot. [n.d.]. SUPPORT - PARROT BEBOP 2. https://support.parrot.com/us/support/products/parrot-bebop-2-fpv/support-produit

[57]

Jarez S Patel, Francesco Fioranelli, and David Anderson. 2018. Review of radar classification and RCS characterisation techniques for small UAVs ordrones. LET Radar, Sonar & Navigation 12, 9 (2018), 911--919.

[58]

Thomas Pathier. 2019. SoundUAV: Towards Delivery Drone Authentication via Acoustic Noise Fingerprinting. In DroNet'19. 27--32.

[59]

Junkai Peng, Changwen Zheng, Tianyu Cui, Ye Cheng, and Lingyu Si. 2018. Using Images Rendered by PBRT to Train Faster R-CNN for UAV Detection. https://doi.org/10.24132/CSRN.2018.2802.3

[60]

Ilyes Rebai, Yessine BenAyed, Walid Mahdi, and Jean-Pierre Lorré. 2017. Improving speech recognition using data augmentation and acoustic model fusion. Procedia Computer Science 112 (2017), 316--322.

Digital Library

[61]

Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. 2015. Faster r-cnn: Towards real-time object detection with region proposal networks. In Advances in neural information processing systems. 91--99.

[62]

Matthew Ritchie, Francesco Fioranelli, Hervé Borrion, and Hugh Griffiths. 2017. Multistatic micro-Doppler radar feature extraction for classification of unloaded/loaded micro-drones. IET Radar, Sonar Navigation 11, 1 (2017), 116--124.

[63]

N. Roy, M. Gowda, and R. Choudhury. 2015. Ripple: Communicating through Physical Vibration. In NSDI.

[64]

Fatemeh Saki, Yinyi Guo, Cheng-Yu Hung, Lae-Hoon Kim, Manyu Deshpande, Sunkuk Moon, Eunjeong Koh, and Erik Visser. 2019. Open-set evolving acoustic scene classification system. (2019).

[65]

Stamatios Samaras, Eleni Diamantidou, Dimitrios Ataloglou, Nikos Sakellariou, Anastasios Vafeiadis, Vasilis Magoulianitis, Antonios Lalas, Anastasios Dimou, Dimitrios Zarpalas, Konstantinos Votis, Petros Daras, and Dimitrios Tzovaras. 2019. Deep Learning on Multi Sensor Data for Counter UAV Applications---A Systematic Review. Sensors 19 (11 2019), 4837. https://doi.org/10.3390/s19224837

[66]

Alexander Sedunov, Darren Haddad, Hady Salloum, Alexander Sutin, Nikolay Sedunov, and Alexander Yakubovskiy. 2019. Stevens Drone Detection Acoustic System and Experiments in Acoustics UAV Tracking. In 2019 IEEE International Symposium on Technologies for Homeland Security (HST). 1--7.

[67]

Zhiguo Shi, Xianyu Chang, Chaoqun Yang, Zexian Wu, and Junfeng Wu. 2020. An Acoustic-Based Surveillance System for Amateur Drones Detection and Localization. IEEE Transactions on Vehicular Technology 69, 3 (2020), 2731--2739.

[68]

Zhiyuan Shi, Minmin Huang, Caidan Zhao, Lianfen Huang, Xiaojiang Du, and Yifeng Zhao. 2017. Detection of LSSUAV using hash fingerprint based SVDD. In 2017 IEEE International Conference on Communications (ICC). 1--5.

[69]

Royal Meteorological Society. [n.d.]. The Beaufort Scale How is wind speed measured? https://www.rmets.org/resource/beaufort-scale

[70]

Martin Strauss, Pol Mordel, Victor Miguet, and Antoine Deleforge. 2018. DREGON: Dataset and Methods for UAV-Embedded Sound Source Localization. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2018). IEEE, Madrid, Spain, 5735--5742. https://doi.org/10.1109/IROS.2018.8593581

[71]

Fredrik Svanstrom, Cristofer Englund, and Fernando Alonso-Fernandez. 2020. Real-Time Drone Detection and Tracking With Visible, Thermal and Acoustic Sensors. arXiv:2007.07396 [cs.CV]

[72]

Bilal Taha and Abdulhadi Shoufan. 2019. Machine Learning-Based Drone Detection and Classification: State-of-the-Art in Research. IEEE Access 7 (2019), 138669--138682.

[73]

Sarah Taillier. 2014. Triathlete injured as drone filming race falls to ground. https://www.abc.net.au/news/2014-04-07/triathlete-injured-as-drone-filming-race-drops-to-ground/5371658

[74]

Charles E. Tinney and Jayant Sirohi. 2018. Multirotor Drone Noise at Static Thrust. AIAA Journal 56 (2018), 2816--2826.

[75]

Zahoor Uddin, Muhammad Altaf, Muhammad Bilal, Lewis Nkenyereye, and Ali Kashif Bashir. 2020. Amateur Drones Detection: A machine learning approach utilizing the acoustic signals in the presence of strong interference. Computer Communications 154 (Mar 2020), 236--245. https://doi.org/10.1016/j.comcom.2020.02.065

[76]

Cornell University. 2020. A General Guide for Deriving Abundance Estimates from Hydroacoustic Data. http://www.acousticsunpacked.org/AcousticBackground/Signal-to-noiseRatio.html

[77]

Eren Unlu, Emmanuel Zenou, and Nicolas Riviere. 2018. Using Shape Descriptors for UAV Detection. Electronic Imaging 2018 (01 2018), 1--5. https://doi.org/10.2352/ISSN.2470-1173.2018.09.SRV-128

[78]

I.Wagner. [n.d.]. Commercial UAVs - Statistics & Facts. arXiv:https://www.statista.com/topics/3601/commercial-uavs/

[79]

Yang Wang, Huichuan Xia, Yaxing Yao, and Yun Huang. 01 Jul. 2016. Flying Eyes and Hidden Controllers: A Qualitative Study of People's Privacy Perceptions of Civilian Drones in The US. Proceedings on Privacy Enhancing Technologies 2016, 3 (01 Jul. 2016), 172--190. https://doi.org/10.1515/popets-2016-0022

[80]

Martin Weil. 2013. Drone crashes into Virginia bull run crowd. https://www.washingtonpost.com/local/drone-crashes-into-virginia-bull-run-crowd/2013/08/26/424e0b9e-0e00-11e3-85b6-d27422650fd5_story.html

[81]

Kevin Wilkinghoff and Frank Kurth. 2019. Open-set acoustic scene classification with deep convolutional autoencoders. (2019).

[82]

Yuhang Wu, Yan-ting Ai, Wang Ze, Tian Jing, Xiang Song, and Yingtao Chen. 2019. A Novel Aerodynamic Noise Reduction Method Based on Improving Spanwise Blade Shape for Electric Propeller Aircraft. International Journal of Aerospace Engineering 2019 (2019), 3750451. https://doi.org/10.1155/2019/3750451

[83]

Shuochao Yao, Shaohan Hu, Yiran Zhao, Aston Zhang, and Tarek Abdelzaher. 2017. Deepsense: A unified deep learning framework for time-series mobile sensing data processing. In Proceedings of the 26th International Conference on World Wide Web. 351--360.

Digital Library

[84]

Yaxing Yao, Huichuan Xia, Yun Huang, and Yang Wang. 2017. Free to Fly in Public Spaces: Drone Controllers' Privacy Perceptions and Practices. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (Denver, Colorado, USA) (CHI '17). Association for Computing Machinery, New York, NY, USA, 6789--6793. https://doi.org/10.1145/3025453.3026049

Digital Library

[85]

Yaxing Yao, Huichuan Xia, Yun Huang, and Yang Wang. 2017. Privacy Mechanisms for Drones: Perceptions of Drone Controllers and Bystanders. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (Denver, Colorado, USA) (CHI '17). Association for Computing Machinery, New York, NY, USA, 6777--6788. https://doi.org/10.1145/3025453.3025907

Digital Library

[86]

Pengfei Zhang, Le Yang, Gao Chen, and Gang Li. 2017. Classification of drones based on micro-Doppler signatures with dual-band radar sensors. 2017 Progress in Electromagnetics Research Symposium -Fall (PIERS -FALL) (2017), 638--643.

[87]

Wenyu Zhang and Gang Li. 2018. Detection of multiple micro-drones via cadence velocity diagram analysis. Electronics Letters 54, 7 (2018), 441--443.

[88]

Adam Zwickle, Hillary B. Farber, and Joseph A. Hamm. 2019. Comparing public concern and support for drone regulation to the current legal framework. Behavioral Sciences & the Law 37, 1 (2019), 109--124. https://doi.org/10.1002/bsl.2357 arXiv:https://onlinelibrary.wiley.com/doi/pdf/10.1002/bsl.2357

Cited By

Itare NThomas JRaoof K(2025)Genetic Algorithm-Based Acoustic Array Optimization for Estimating UAV DOA Using BeamformingDrones10.3390/drones90201499:2(149)Online publication date: 18-Feb-2025
https://doi.org/10.3390/drones9020149
Mejia-Herrera MBotero-Valencia JOrtega JHernández-García R(2025)Development of a Solar-Powered Edge Processing Perimeter Alert System with AI and LoRa/LoRaWAN Integration for Drone Detection and Enhanced SecurityDrones10.3390/drones90100439:1(43)Online publication date: 10-Jan-2025
https://doi.org/10.3390/drones9010043
Zhang MLu LWu YYan ZSun JLin FRen K(2025)DroneAudioID: A Lightweight Acoustic Fingerprint-Based Drone Authentication System for Secure Drone DeliveryIEEE Transactions on Information Forensics and Security10.1109/TIFS.2025.352781420(1447-1461)Online publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1109/TIFS.2025.3527814
Show More Cited By

Index Terms

DronePrint: Acoustic Signatures for Open-set Drone Detection and Identification with Online Data
1. Computing methodologies
  1. Machine learning
    1. Machine learning algorithms
      1. Feature selection
    2. Machine learning approaches
      1. Neural networks

Recommendations

Drone Authentication via Acoustic Fingerprint
ACSAC '22: Proceedings of the 38th Annual Computer Security Applications Conference

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-...
AcousticPrint: acoustic signature based open set drone identification
WiSec '20: Proceedings of the 13th ACM Conference on Security and Privacy in Wireless and Mobile Networks

Malicious or improper use of drones can pose significant privacy and security threats in both civilian and military settings. There are many situations where it requires to detect the presence of a drone and identify the exact model to be used in ...
Drones and Privacy

Drones, also referred to as UAV's Unmanned Aerial Vehicle, are an aircraft without a human pilot. Drones have been used by various military organisations for over a decade, but in recent years drones a have been emerging more and more in commercial and ...

Comments

Information & Contributors

Information

Published In

cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 5, Issue 1

March 2021

1272 pages

EISSN:2474-9567

DOI:10.1145/3459088

Issue’s Table of Contents

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 March 2021

Published in IMWUT Volume 5, Issue 1

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed

Funding Sources

NSW Defence Innovation Network and NSW State Government

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

29
Total Citations
View Citations
876
Total Downloads

Downloads (Last 12 months)191
Downloads (Last 6 weeks)30

Reflects downloads up to 28 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Itare NThomas JRaoof K(2025)Genetic Algorithm-Based Acoustic Array Optimization for Estimating UAV DOA Using BeamformingDrones10.3390/drones90201499:2(149)Online publication date: 18-Feb-2025
https://doi.org/10.3390/drones9020149
Mejia-Herrera MBotero-Valencia JOrtega JHernández-García R(2025)Development of a Solar-Powered Edge Processing Perimeter Alert System with AI and LoRa/LoRaWAN Integration for Drone Detection and Enhanced SecurityDrones10.3390/drones90100439:1(43)Online publication date: 10-Jan-2025
https://doi.org/10.3390/drones9010043
Zhang MLu LWu YYan ZSun JLin FRen K(2025)DroneAudioID: A Lightweight Acoustic Fingerprint-Based Drone Authentication System for Secure Drone DeliveryIEEE Transactions on Information Forensics and Security10.1109/TIFS.2025.352781420(1447-1461)Online publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1109/TIFS.2025.3527814
Famili AStavrou AWang HPark JGerdes R(2024)Securing Your Airspace: Detection of Drones Trespassing Protected AreasSensors10.3390/s2407202824:7(2028)Online publication date: 22-Mar-2024
https://doi.org/10.3390/s24072028
Hadi GRamesh SChan M(2024)PADrone: Pre-flight Abnormalities Detection on Drone via Deep RF SensingACM Transactions on Internet of Things10.1145/37061216:1(1-30)Online publication date: 29-Nov-2024
https://dl.acm.org/doi/10.1145/3706121
Dafrallah SAkhloufi M(2024)Malicious UAV detection using various modalitiesDrone Systems and Applications10.1139/dsa-2023-004912(1-18)Online publication date: 1-Jan-2024
https://doi.org/10.1139/dsa-2023-0049
Sun YWang WMottola LZhang JWang RHe Y(2024)Indoor Drone Localization and Tracking Based on Acoustic Inertial MeasurementIEEE Transactions on Mobile Computing10.1109/TMC.2023.333586023:6(7537-7551)Online publication date: Jun-2024
https://doi.org/10.1109/TMC.2023.3335860
Zhang QZeng FHu JLiu DKuang LXiao ZJiang H(2024)E-Argus: Drones Detection by Side-Channel Signatures via Electromagnetic RadiationIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.343297725:11(18978-18991)Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1109/TITS.2024.3432977
Naeini SSimmatis LJafari DYunusova YTaati B(2024)Improving Dysarthric Speech Segmentation With Emulated and Synthetic AugmentationIEEE Journal of Translational Engineering in Health and Medicine10.1109/JTEHM.2024.337532312(382-389)Online publication date: 2024
https://doi.org/10.1109/JTEHM.2024.3375323
Ghazlane YGmira MMedromi H(2024)Development Of A Vision- based Anti-drone Identification Friend Or Foe Model To Recognize Birds And Drones Using Deep LearningApplied Artificial Intelligence10.1080/08839514.2024.231867238:1Online publication date: 17-Feb-2024
https://doi.org/10.1080/08839514.2024.2318672
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Issue’s Table of Contents