A machine learning based attack in UAV communication networks

Xiao Chun Chen, Yu Jia Chen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Scopus citations

Abstract

With the advantages of agility and mobility, unmanned aerial vehicles (UAVs) have been widely applied for various civil and military missions. To dynamically control and monitor UAV, it is necessary to broadcast their location information. However, flying in the aerial environment and the fixed operation location also make UAV communications more vulnerable to privacy attacks. In this paper, we present the machine learning (ML)-based attack of UAV-based wireless networks when an attacker can obtain both plaintext and ciphertext. The collected plaintext-ciphertext pairs can be used to train an ML classifier which can help decrypt the UAV messages. By simulations, we show that a simple neural network (NN) can decrypt UAV location data with high probability. Finally, we conclude the work and present a network coding based encryption scheme as our future research direction.

Original languageEnglish
Title of host publication2019 IEEE 90th Vehicular Technology Conference, VTC 2019 Fall - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728112206
DOIs
StatePublished - Sep 2019
Event90th IEEE Vehicular Technology Conference, VTC 2019 Fall - Honolulu, United States
Duration: 22 Sep 201925 Sep 2019

Publication series

NameIEEE Vehicular Technology Conference
Volume2019-September
ISSN (Print)1550-2252

Conference

Conference90th IEEE Vehicular Technology Conference, VTC 2019 Fall
Country/TerritoryUnited States
CityHonolulu
Period22/09/1925/09/19

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