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

TL;DR

This paper presents a deep learning-based method utilizing convolutional attention mechanisms to detect UAV network attacks over fading channels and interference, enhancing security in 5G/6G scenarios with real-time attack recognition capabilities.

Contribution

It introduces a novel deep learning approach that detects UAV network attacks using RSSI and SINR parameters, capable of identifying multiple attackers in complex environments without prior attack data.

Findings

Detects attacks within 100 ms after initiation

Identifies all attackers among 20 terrestrial users

Successfully detects moving attackers at 500 meters

Abstract

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 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 in a city. 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 in 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 prospective 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 show 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. Our algorithm also detects moving attackers from a distance of 500 m.