Can the Operator of a Drone Be Located by Following the Drone’s Path?

Eliyahu Mashhadi; Yossi Oren; Gera Weiss

doi:10.1007/978-3-030-49785-9_6

Can the Operator of a Drone Be Located by Following the Drone’s Path?

Eliyahu Mashhadi, Yossi Oren, Gera Weiss

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

Small commercial Unmanned Aerial Systems (UASs), called drones in common language, pose significant security risks due to their agility, high availability and low price. There is, therefor, a growing need to develop methods for detection, localization and mitigation of malicious and other harmful operation of these drones. This paper presents our work towards autonomously localizing drone operators based only on following their path in the sky. We use a realistic simulation environment and collect the path of the drone when flown from different points of view. A deep neural network was trained to be able to predict the location of drone operators, given the path of the drones. The model is able to achieve prediction of the location of the location of the operator with 73% accuracy.

Original language	English
Title of host publication	Cyber Security Cryptography and Machine Learning - 4th International Symposium, CSCML 2020, Proceedings
Editors	Shlomi Dolev, Gera Weiss, Vladimir Kolesnikov, Sachin Lodha
Publisher	Springer
Pages	85-93
Number of pages	9
ISBN (Print)	9783030497842
DOIs	https://doi.org/10.1007/978-3-030-49785-9_6
State	Published - 1 Jan 2020
Event	4th International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2020 - Beersheba, Israel Duration: 2 Jul 2020 → 3 Jul 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12161 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	4th International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2020
Country/Territory	Israel
City	Beersheba
Period	2/07/20 → 3/07/20

Keywords

Deep learning
Deep neural network
Drone
Security
Surveillance
UAS

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-49785-9_6

Cite this

Mashhadi, E., Oren, Y., & Weiss, G. (2020). Can the Operator of a Drone Be Located by Following the Drone’s Path? In S. Dolev, G. Weiss, V. Kolesnikov, & S. Lodha (Eds.), Cyber Security Cryptography and Machine Learning - 4th International Symposium, CSCML 2020, Proceedings (pp. 85-93). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12161 LNCS). Springer. https://doi.org/10.1007/978-3-030-49785-9_6

Mashhadi, Eliyahu ; Oren, Yossi ; Weiss, Gera. / Can the Operator of a Drone Be Located by Following the Drone’s Path?. Cyber Security Cryptography and Machine Learning - 4th International Symposium, CSCML 2020, Proceedings. editor / Shlomi Dolev ; Gera Weiss ; Vladimir Kolesnikov ; Sachin Lodha. Springer, 2020. pp. 85-93 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Can the Operator of a Drone Be Located by Following the Drone{\textquoteright}s Path?",

abstract = "Small commercial Unmanned Aerial Systems (UASs), called drones in common language, pose significant security risks due to their agility, high availability and low price. There is, therefor, a growing need to develop methods for detection, localization and mitigation of malicious and other harmful operation of these drones. This paper presents our work towards autonomously localizing drone operators based only on following their path in the sky. We use a realistic simulation environment and collect the path of the drone when flown from different points of view. A deep neural network was trained to be able to predict the location of drone operators, given the path of the drones. The model is able to achieve prediction of the location of the location of the operator with 73% accuracy.",

keywords = "Deep learning, Deep neural network, Drone, Security, Surveillance, UAS",

author = "Eliyahu Mashhadi and Yossi Oren and Gera Weiss",

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Mashhadi, E, Oren, Y & Weiss, G 2020, Can the Operator of a Drone Be Located by Following the Drone’s Path? in S Dolev, G Weiss, V Kolesnikov & S Lodha (eds), Cyber Security Cryptography and Machine Learning - 4th International Symposium, CSCML 2020, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12161 LNCS, Springer, pp. 85-93, 4th International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2020, Beersheba, Israel, 2/07/20. https://doi.org/10.1007/978-3-030-49785-9_6

Can the Operator of a Drone Be Located by Following the Drone’s Path? / Mashhadi, Eliyahu; Oren, Yossi ; Weiss, Gera.
Cyber Security Cryptography and Machine Learning - 4th International Symposium, CSCML 2020, Proceedings. ed. / Shlomi Dolev; Gera Weiss; Vladimir Kolesnikov; Sachin Lodha. Springer, 2020. p. 85-93 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12161 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review