A Convolutional Attention Based Deep Learning Solution for 5G UAV Network Attack Recognition over Fading Channels and Interference
; Farkhari, H. F
; Campos, L. Campos
; Koutlia, K.
; lagén, S.
; Bernardo , L.B.
A Convolutional Attention Based Deep Learning Solution for 5G UAV Network Attack Recognition over Fading Channels and Interference, Proc Vehicular Technology Conference: VTC-Fall, Londres, United Kingdom, Vol. , pp. - , September, 2022.
Digital Object Identifier:
When users exchange data with Unmanned Aerial
Vehicles - (UAVs) over Air-to-Ground - (A2G) wireless commu-
nication networks, they expose the link to attacks that could
increase packet loss and might disrupt connectivity. For example,
in emergency deliveries, losing control information (i.e., data
related to the UAV control communication) might result in
accidents that cause UAV destruction and damage to buildings
or other elements. To prevent these problems, these issues must
be addressed in 5G and 6G scenarios. This research offers a
Deep Learning (DL) approach for detecting attacks on UAVs
equipped with Orthogonal Frequency Division Multiplexing -
(OFDM) receivers on Clustered Delay Line (CDL) channels
in highly complex scenarios involving authenticated terrestrial
users, as well as attackers in unknown locations. We use the
two observable parameters available in 5G UAV connections:
the Received Signal Strength Indicator (RSSI) and the Signal to
Interference plus Noise Ratio (SINR). The developed algorithm
is generalizable regarding attack identification, which does not
occur during training. Further, it can identify all the attackers in
the environment with 20 terrestrial users. A deeper investigation
into the timing requirements for recognizing attacks shows that
after 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 the authenticated UAV uses. The developed
algorithm also detects moving attackers from a distance of 500m.