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A Synthetic Dataset for 5G UAV Attacks Based on Observable Network Parameters
Joseanne Viana, Hamed Farkhari, Pedro Sebastiao +4
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. Th…
Accurate and Reliable Methods for 5G UAV Jamming Identification With Calibrated Uncertainty
Hamed Farkhari, Joseanne Viana, Pedro Sebastiao +4
Only increasing accuracy without considering uncertainty may negatively impact Deep Neural Network (DNN) decision-making and decrease its reliability. This paper proposes five comb…
A Convolutional Attention Based Deep Network Solution for UAV Network Attack Recognition over Fading Channels and Interference
Joseanne Viana, Hamed Farkhari, Luis Miguel Campos +5
When users exchange data with Unmanned Aerial vehicles - (UAVs) over air-to-ground (A2G) wireless communication networks, they expose the link to attacks that could increase packet…
Two methods for Jamming Identification in UAVs Networks using New Synthetic Dataset
Joseanne Viana, Hamed Farkhari, Luis Miguel Campos +4
Unmanned aerial vehicle (UAV) systems are vulnerable to jamming from self-interested users who utilize radio devices for their benefits during UAV transmissions. The vulnerability…