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Synthetic datasets are beneficial for machine learning researchers due to thepossibility of experimenting with new strategies and algorithms in the trainingand testing phases. These datasets can easily include more scenarios that mightbe 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 beused in training while testing can be done with real data. In this paper, wepresent, to the best of our knowledge, the first synthetic dataset for UnmannedAerial Vehicle (UAV) attacks in 5G and beyond networks based on the followingkey observable network parameters that indicate power levels: the ReceivedSignal Strength Indicator (RSSI) and the Signal to Interference-plus-NoiseRatio (SINR). The main objective of this data is to enable deep networkdevelopment for UAV communication security. Especially, for algorithmdevelopment or the analysis of time-series data applied to UAV attackrecognition. Our proposed dataset provides insights into network functionalitywhen static or moving UAV attackers target authenticated UAVs in an urbanenvironment. The dataset also considers the presence and absence ofauthenticated terrestrial users in the network, which may decrease the deepnetworks ability to identify attacks. Furthermore, the data provides deepercomprehension of the metrics available in the 5G physical and MAC layers formachine learning and statistics research. The dataset will available at linkarchive-beta.ics.uci.edu

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