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When users exchange data with Unmanned Aerial vehicles - (UAVs) overair-to-ground (A2G) wireless communication networks, they expose the link toattacks that could increase packet loss and might disrupt connectivity. Forexample, in emergency deliveries, losing control information (i.e data relatedto the UAV control communication) might result in accidents that cause UAVdestruction and damage to buildings or other elements in a city. To preventthese problems, these issues must be addressed in 5G and 6G scenarios. Thisresearch offers a deep learning (DL) approach for detecting attacks in UAVsequipped with orthogonal frequency division multiplexing (OFDM) receivers onClustered Delay Line (CDL) channels in highly complex scenarios involvingauthenticated terrestrial users, as well as attackers in unknown locations. Weuse the two observable parameters available in 5G UAV connections: the ReceivedSignal Strength Indicator (RSSI) and the Signal to Interference plus NoiseRatio (SINR). The prospective algorithm is generalizable regarding attackidentification, which does not occur during training. Further, it can identifyall the attackers in the environment with 20 terrestrial users. A deeperinvestigation into the timing requirements for recognizing attacks show thatafter training, the minimum time necessary after the attack begins is 100 ms,and the minimum attack power is 2 dBm, which is the same power that theauthenticated UAV uses. Our algorithm also detects moving attackers from adistance of 500 m.

A Convolutional Attention Based Deep Network Solution for UAV Network Attack Recognition over Fading Channels and Interference