3D Radio Map-based GPS spoofing Detection and Mitigation for Cellular-Connected UAVs

Tutkimustuotos: LehtiartikkeliArticleScientificvertaisarvioitu

63 Lataukset (Pure)

Abstrakti

With the upcoming 5G and beyond wireless communication
system, cellular-connected Unmanned Aerial Vehicles
(UAVs) are emerging as a new pattern to give assistance for
target searching, emergency rescue, and network recovery. Such
cellular-connected UAV systems highly rely on accurate and
secure navigation systems, e.g. the Globe Navigation System
(GPS). However, civil GPS services are unencrypted and vulnerable
to spoofing attacks that can manipulate UAVs’ location
and abort the UAVs’ mission. This paper leverage 3D radio
map and machine learning methods to detect and mitigate GPS
spoofing attacks for cellular-connected UAVs. Precisely, the edge
UAV flight controller uses ray tracing tools deterministic channel
models, and Kriging methods to construct a theoretical 3D radio
map. Then the machine learning methods, such as Multi-Layer
Perceptrons (MLP), Convolutional Neural Networks (CNN), and
Recurrent Neural Networks (RNN), are employed to detect GPS
spoofing by analyzing the UAV/base station reported Received
Signal Strength (RSS) values and the theoretical radio map RSS
values. Once spoofing is detected, the particle filter is applied to
relocate the UAV and mitigate GPS deviation. The experiment
results indicate that the Universal Kriging (UK) with exponential
covariance function has the lowest standard errors for radio map
construction. Moreover, the MLP achieves the highest spoofing
detection accuracy with different spoofing margins because of the
statistic prepossessing relieving environmental impacts, while the
CNN has a comparable detection accuracy with less training time
than MLP since CNN inputs are raw RSS data. Furthermore,
the particle filter-based GPS spoofing mitigation can relocate the
UAV to the real position within an error of 10 meters using 100
particles.
AlkuperäiskieliEnglanti
Sivumäärä15
JulkaisuIEEE Transactions on Machine Learning in Communications and Networking
Vuosikerta1
DOI - pysyväislinkit
TilaJulkaistu - 2023
OKM-julkaisutyyppiA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä

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