TITLE: A Synthetic Dataset for 5G UAV Attacks Based on Observable Network Parameters 

ABSTRACT: Synthetic datasets are beneficial for machine learning researchers due to the
possibility of experimenting with new strategies and algorithms in the training
and testing phases. These datasets can easily include more scenarios that might
be costly to research with real data or can complement and, in some cases,
replace real data measurements, depending on the quality of the synthetic data.
They can also solve the unbalanced data problem, avoid overfitting, and can be
used in training while testing can be done with real data. In this paper, we
present, to the best of our knowledge, the first synthetic dataset for Unmanned
Aerial Vehicle (UAV) attacks in 5G and beyond networks based on the following
key observable network parameters that indicate power levels: the Received
Signal Strength Indicator (RSSI) and the Signal to Interference-plus-Noise
Ratio (SINR). The main objective of this data is to enable deep network
development for UAV communication security. Especially, for algorithm
development or the analysis of time-series data applied to UAV attack
recognition. Our proposed dataset provides insights into network functionality
when static or moving UAV attackers target authenticated UAVs in an urban
environment. The dataset also considers the presence and absence of
authenticated terrestrial users in the network, which may decrease the deep
networks ability to identify attacks. Furthermore, the data provides deeper
comprehension of the metrics available in the 5G physical and MAC layers for
machine learning and statistics research. The dataset will available at link
archive-beta.ics.uci.edu
Synthetic datasets are beneficial for machine learning researchers due to the
possibility of experimenting with new strategies and algorithms in the training
and testing phases. These datasets can easily include more scenarios that might
be costly to research with real data or can complement and, in some cases,
replace real data measurements, depending on the quality of the synthetic data.
They can also solve the unbalanced data problem, avoid overfitting, and can be
used in training while testing can be done with real data. In this paper, we
present, to the best of our knowledge, the first synthetic dataset for Unmanned
Aerial Vehicle (UAV) attacks in 5G and beyond networks based on the following
key observable network parameters that indicate power levels: the Received
Signal Strength Indicator (RSSI) and the Signal to Interference-plus-Noise
Ratio (SINR). The main objective of this data is to enable deep network
development for UAV communication security. Especially, for algorithm
development or the analysis of time-series data applied to UAV attack
recognition. Our proposed dataset provides insights into network functionality
when static or moving UAV attackers target authenticated UAVs in an urban
environment. The dataset also considers the presence and absence of
authenticated terrestrial users in the network, which may decrease the deep
networks ability to identify attacks. Furthermore, the data provides deeper
comprehension of the metrics available in the 5G physical and MAC layers for
machine learning and statistics research. The dataset will available at link
archive-beta.ics.uci.edu
A Synthetic Dataset for 5G UAV Attacks Based on Observable Network Parameters
http://arxiv.org/abs/2211.09706v1